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July 11, 2016 09:14
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Tweets from 7th Sheffield Conference on Chemoinformatics #ShefChem16
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| nathanbroo @WendyAnneWarr @baoilleach @mvkrier @rguha awesome! We have a go! #ShefChem16. Looking forward to it! | |
| dgelemi @WendyAnneWarr @nathanbroon @baoilleach @mvkrier @rguha nice. See you at #ShefChem16 on Monday | |
| nathanbroo Hashtag for Sheffield #Chemoinformatics conference is #ShefChem16 | |
| http://cisrg.shef.ac.uk/shef2016 | |
| #CompChem #RealTimeChempic.twitter.com/8J2fU9eZwZ | |
| conViktion Sorry to have to miss out on #ShefChem16. | |
| I hope it's a great event! | |
| nathanbroo Using #ShefChem16 for Sheffield Chemoinformatics conference. @OpenEyeSoftware @CCG_MOE @ccdc_cambridge @ChEMBL @3dsBIOVIA @SimulationsPlus | |
| mvkrier #ShefChem16 here we come! I'm transiting via Manchester on Monday morning. Anyone else? https://twitter.com/nathanbroon/status/749191728956071936 | |
| nathanbroo Using #ShefChem16 for Sheffield Chemoinformatics conference. @cressetgroup @nmsoftware @MolInformatics @JCIM_ACS @Molomics @ChemDraw | |
| nathanbroo Using #ShefChem16 for Sheffield Chemoinformatics conference. @macinchem @RSC_CICAG @GDCh_CIC | |
| WendyAnneW @dgelemi @nathanbroon @baoilleach @mvkrier @rguha #ShefChem16 Driving from Cheshire on Monday morning. Looking forward to great conference | |
| georgeisyo @dgelemi early start for me with the 07:24 train from St Pancras. #ShefChem16 | |
| ZINClick Moving from #medchemie in Dublin to Sheffield... -2 days #ShefChem16 | |
| nathanbroo People to follow for #ShefChem16: @WendyAnneWarr @drjohndholliday @baoilleach @DrJoshuaBox @mvkrier @georgeisyourman @dgelemi | |
| nathanbroo I've setup a @lanyrd page for the #ShefChem16 conference so you can follow online: http://lanyrd.com/cgkxwk | |
| mvkrier Looking forward to hear from Christos Nicolaou at #ShefChem16 https://twitter.com/jcim_acs/status/748214524486914048 | |
| mvkrier #ShefChem16 Don't miss Andrew's talk (#15): Calvin Mooers & the Early History of Chemical Information https://twitter.com/mvkrier/status/628279344708866048 | |
| mvkrier @ssalentin Looking forward to your talk ( #19 ) at #ShefChem16 https://twitter.com/F1000Research/status/680767705704722432 | |
| mvkrier @GJPvWesten Looking forward to tweetup at #ShefChem16 | |
| drjohndhol Have a safe journey to #ShefChem16 - See you all tomorrow | |
| georgeisyo On the train to my alma mater, representing the @ChEMBL group with @mmmnow. Can't believe this is my 4th Sheffield conference! #ShefChem16 | |
| dgelemi On my way to #ShefChem16, but will miss @georgeisyourman talk after delayed train and missed connection. | |
| cressetgro Today we present on 'Examining the Diversity of Large Collections of Building Blocks in 3D' at Sheffield Chemoinformatics conf. #ShefChem16 | |
| GJPvWesten At #ShefChem16 , interesting line up of talks! | |
| mvkrier Waiting for the train at MIA to take to #shefchem16 | |
| nathanbroo Nice relaxing breakfast and. Pe ready to enjoy #ShefChem16. | |
| dr_greg_la This will be my first Sheffield meeting. Crazy. | |
| #ShefChem16 | |
| EnricoBera Excited for the Sheffield meeting! #ShefChem16 | |
| nathanbroo Prof. Peter Willett is opening #ShefChem16 with a history of the conference over the years. | |
| stewartadc Peter Willet is now opening #ShefChem16. I just gave wireless key to @WendyAnneWarr so I'm expecting some insightful live tweets! | |
| nathanbroo First up is @georgeisyourman talking about @ChEMBL, @SureChEMBL & @Open_PHACTS. #ShefChem16 | |
| dr_greg_la And we kick off with @georgeisyourman talking about @ChEMBL and @SureChEMBL #ShefChem16 | |
| baoilleach #shefchem16 Sheffield Conference on Cheminformatics | |
| WendyAnneW @stewartadcock #ShefChem16 Willett with 2 t's. Not insightful... Sorry | |
| baoilleach #shefchem16 @georgeisyourman on SureChEMBl | |
| WendyAnneW #ShefChem16 mining began with GENSAL | |
| baoilleach #shefchem16 Patent knowledge may never appear elsewhere, and even if it does, there's a lag. First disclosure often in patent. | |
| baoilleach #shefchem16 2007 SureChem. 2010 Macmillan purchase. 2013 donated to EMBL-EBI. 2015 bioannotations. | |
| WendyAnneW #ShefChem16 SureChembl data now in Open PHACTS | |
| baoilleach #shefchem16 Structures from text, from images and from provided MOL files. Fully automatic - no manual curation. | |
| WendyAnneW #ShefChem16 SureCHEMBL tiresome syntax to tweet. I will cheat | |
| baoilleach #shefchem16 Can do fuzzy substructure search, e.g. positional variation with halogen. | |
| nathanbroo .@SureChEMBL allows structure and substructure search from automatically extract chemistry patent data. #ShefChem16 | |
| baoilleach #shefchem16 Not just exemplified structures, but reagents, intermediates, solvents. Don't yet handle Markush though. | |
| WendyAnneW #ShefChem16 Markush not handled yet | |
| baoilleach #shefchem16 18M unique structures. ~80k novel cmpds per month. 1-7 days from publication to entry into SureChEMBL. | |
| WendyAnneW #ShefChem16 1 to 7 days only before patent appears in SureChembl | |
| baoilleach #shefchem16 Comparison to SciFinder. Set of 47 patents chosen. 65% of SF cmpds were found by SureChEMBL. Missed were Markush, or from tables | |
| WendyAnneW #ShefChem16 65% of scifinder cmpds are found in SureChembl | |
| baoilleach #shefchem16 ..but these patents were quite old, with OCR mistakes, so 65% is lower bound. | |
| nathanbroo Assessment of chemistry databases generated by automated extraction of chemical structures from patents. http://www.ncbi.nlm.nih.gov/m/pubmed/26457120/ #ShefChem16 | |
| WendyAnneW #ShefChem16 the missing 35% caused by OCR errors etc Or out of scope | |
| baoilleach #shefchem16 Can download 'map file' of cmpd to patent info. Or via UniChem, and or a data client feed to keep up to date incrementally. | |
| WendyAnneW #ShefChem16 unichem has 135 million struts | |
| baoilleach #shefchem16 Bio-annotations made with Termite (SciBite) run over life-science patents. Finds genes, diseases, frequencies in different parts | |
| baoilleach #shefchem16 ...also relevance score (0-3) to remove noise as inventors often mention large amounts of diseases/proteins, e.g. over 50 | |
| WendyAnneW #ShefChem16 oops. Structures | |
| nathanbroo Bio annotation in @SureChEMBL using Termite text-mining engine #ShefChem16 | |
| baoilleach #shefchem16 ...same for compounds. Real scope versus large numbers of mentioned drugs. | |
| baoilleach #shefchem16 To do this, use term frequencies but also the position of the entity, e.g. in title or caption or heading. | |
| WendyAnneW #ShefChem16 relevance ranking by freq and position of occurrence of bio term etc. (SureChembl) | |
| baoilleach #shefchem16 Compounds with busy chemical space around them are interesting. cf. Hatori et al, Tyrchan et al. | |
| nathanbroo Using FCFP_4 fingerprints to identify nearest neighbours of relevance. Wonder if too fuzzy? #ShefChem16 | |
| WendyAnneW #ShefChem16 JCIM vol 52 p 1480 | |
| nathanbroo .@Open_PHACTS offers linked databases in an easy-to-use API. #ShefChem16 | |
| WendyAnneW #ShefChem16 DDT vol 17, 21 | |
| baoilleach #ShefChem16 All integrated with OpenPHACTS so can query via its API on disease, target, compound. | |
| baoilleach #ShefChem16 Can infer links between cmpds/targets or disease/targets etc. using Open Phacts API. (Example shown) | |
| nathanbroo Use cases of patent searching: from patent to genes, targets, diseases & structure. #ShefChem16 | |
| WendyAnneW #ShefChem16 SureChembl use case 1targets and diseases using openphacts API. Go to patent. Extract MCS | |
| nathanbroo MCS is a good approximation for manually curated Markush structures in patents. #ShefChem16 | |
| baoilleach #ShefChem16 MCS of structures and Markush structure often very similar. | |
| WendyAnneW #ShefChem16 use case 2. targets and indications. Search the patent one | |
| baoilleach #ShefChem16 eluxadoline - 17 patents in the "patentome" - extract relevant targets and indications | |
| nathanbroo Now possible to link structured data with @SureChEMBL & @Open_PHACTS #ShefChem16 | |
| baoilleach #ShefChem16 Uses: target validation/druggaility, novelty checking, add parmacology and pathway info via ChEMBL | |
| nathanbroo Next steps: target validation, druggability, novelty checking & due diligence. @SureChEMBL #ShefChem16 | |
| WendyAnneW #ShefChem16 it is now poss to access the patent corpus by high thruput mining. Can do target validn. Do novelty check. | |
| nathanbroo .@ChEMBL v22 will have bioactivity data mined from medicinal chemistry patents. #ShefChem16 | |
| baoilleach #ShefChem16 ChEMBL22! BindingDB info - only 5% in ChEMBL already. Many targets not well represented in ChEMBL. Patent activity info. | |
| baoilleach #ShefChem16 @georgeisyourman has delivered what is surely the best talk of the conference so far | |
| nathanbroo Q from the floor: comparison of @ChEMBL & IBM. IBM slightly better in recent study. #ShefChem16 | |
| nathanbroo Q: can database overlaps be used to estimate errors? Yes & no… insufficient reference sets. #ShefChem16 | |
| baoilleach #ShefChem16 BindingDB is manually extracting activity info from patents | |
| nathanbroo Next up is Marc C. Nicklaus from @theNCI talking about SAVI: Synthetically Accesible Virtual Inventory #ShefChem16 | |
| baoilleach #ShefChem16 Marc Nicklaus on SAVI - synthetically acessible virtual inventory - Yuri Pevzner and WDI | |
| mvkrier #ShefChem16 Now Marc Nicklaus from @theNCI about SAVI | |
| baoilleach #ShefChem16 Screening database sizes - what size is enough? NCI ~0.3m, ChemNavigator 3m | |
| DrJoshuaBo Great talk from @georgeisyourman on @SureChEMBL. Will this make companies even sneakier when drafting new patents? #ShefChem16 | |
| nathanbroo Medicinal chemistry relevant structure space is incredibly vast > 10^40 unique structures #ShefChem16 | |
| baoilleach #ShefChem16 SCSORS (semi-custom synthesis online request system) - attrition rate high - failures, low yields, too much time/money lost | |
| baoilleach #ShefChem16 Not "what is a potential bioactive molecule? then make it", instead "what can I easily and cheaply make/get?" | |
| baoilleach #ShefChem16 Let's try for a billion: some chemistry rules, inexpensive starting materials, cheminf engine | |
| WendyAnneW #shefchem16 getting in a mess with mobile app️ | |
| baoilleach #ShefChem16 Hartenfeller reaction rules, but lack chemical context. Worked with Lhasa to create rules. Cactvs for cheminf. | |
| WendyAnneW #shefchem16 Marc Nicklaus using Lhasa rules and CACTVS | |
| baoilleach #ShefChem16 2312 retro syn transforms based on Corey's work in 70s. Written in CHMTRN/PATRAN - an "Interesting" language. | |
| nathanbroo 2,312 retro synthetic transformations based on E. J. Corey's work in the 70s: combination of British English & 70s FORTRAN. #ShefChem16 | |
| mvkrier #ShefChem16 M. Nicklaus: Hartenfeller paper describes robust reactions, but are lacking chemical context information | |
| baoilleach #ShefChem16 One new transform added: azide-alkyne Huisgen cu catalysed cycloaddition - "click chemistry". More to come. | |
| dgelemi @WendyAnneWarr should do like @baoilleach and use laptop #typewriter #fasttyping #ShefChem16 | |
| WendyAnneW #ShefChem16 Marc shows CHMTRN transform | |
| mvkrier #ShefChem16 M. Nicklaus: we are forward synthetic! | |
| drjohndhol #ShefChem16 well under way. A good response to our first presentation. | |
| baoilleach #ShefChem16 Originally for retrosyn but adapted for forward direction. | |
| WendyAnneW #ShefChem16 starting materials from sigma Aldrich | |
| baoilleach #ShefChem16 Using Sigma-Aldrich building blocks - 0.4m high availablity | |
| WendyAnneW #ShefChem16 Marc shows Lhasa react workflow | |
| baoilleach #ShefChem16 Paal-Knorr pyrrole synthesis example, same reactants but different products and scores | |
| baoilleach #ShefChem16 2015 created 0.6m available for download based on 11 "productive" transforms in one-step reactions | |
| baoilleach #ShefChem16 side-comment on PAINS filtering is not good - not explained | |
| nathanbroo "PAINS is not good. You shouldn't use it." In its current form, couldn't agree more!!! #ShefChem16 | |
| baoilleach #ShefChem16 possible products based on 14 rules and 337k starting is 420m, 330m after filtering | |
| WendyAnneW #ShefChem16 Marc 's prods generated as SDfiles. Then apply PAINS etc. 420 million poss prods from 377K build blocks | |
| cressetgro Drop by our booth to see case studies and find out about our academic program #ShefChem16 #InnovativeSoftwarepic.twitter.com/8aRgO51kcE | |
| baoilleach #ShefChem16 combinational explosion - need to filter - non-flatness, TPSA, types of rings/scaffold, rule-of-X, Bruns&Watson rules (JMC2012) | |
| WendyAnneW #ShefChem16 JMC 2012 55 9763 Lilly rules for transformations | |
| nathanbroo Uses Bruns & Watson 275 rules from Lilly for undesirable structures. http://pubs.acs.org/doi/abs/10.1021/jm301008n #ShefChem16 #OpenAccesspic.twitter.com/h3wanzPUnK | |
| baoilleach #ShefChem16 Are novel? Only 0.8% found in PubChem 2015. | |
| nathanbroo Must be clear they are demerits *not* rules or filters! I don't like rules! #ShefChem16 | |
| baoilleach #ShefChem16 looking at stat distributions. Making new rings? Yes - both aromatic and aliphatic. | |
| WendyAnneW #shefchem16 Prop distributions and distrib of cost of starting mats shown. No exotic cmpds. New rings made | |
| baoilleach #ShefChem16 Novel rings? Comparison with Ertl et al (2006). Some novel - missed the details... | |
| WendyAnneW #shefchem16 Marc. Novel space is generated | |
| nathanbroo Analysis of novel rings using Peter Ertl's excellent Quest for the Rings paper. http://pubs.acs.org/doi/abs/10.1021/jm060217p #ShefChem16 pic.twitter.com/r9ODgu4wqe | |
| baoilleach #ShefChem16 New transforms being developed by LHASA. Goal still for ~1bn interesting cmpds. Will be available freely, searchable, orderable | |
| baoilleach #ShefChem16 (Meta: Battery about to run out) Collab between Xemistry, Marc, Lhasa, Novartis, Sigma and Merck. | |
| dgelemi @WendyAnneWarr @baoilleach tweets will go down through the day with fingers and batteries dying #ShefChem16 . Next talk on solubility... | |
| nathanbroo Next up: Solubility - in search of a structural solution. Beth Thomas @ccdc_cambridge #ShefChem16 | |
| WendyAnneW #ShefChem16 Beth Thomas CCDC on solubility | |
| nathanbroo 40% of drugs are practically insoluble! Poor absorption, low target exposure, higher dose. #ShefChem16 | |
| stewartadc Beth Thomas of @ccdc_cambridge standing up to talk about solubility and the data available in the CSD. #shefchem16 pic.twitter.com/BRSyLTXUL6 | |
| nathanbroo General Solubility Equation requires melting points but difficult to predict. #ShefChem16 | |
| nathanbroo Solubility is balance of melting point and lipophilicity. Crystal lattice and 'happiness' in water #ShefChem16 | |
| WendyAnneW #ShefChem16 we should predict solubility cos for design we need understanding | |
| dgelemi #ShefChem16 very few effort from pharma at doing compound X-Ray to understand solubility at the early stage of the project | |
| mvkrier #shefchem16 Beth Thomas from @ccdc_cambridge : Let's see if we can improve solubility by design | |
| nathanbroo Solubility decision tree is presented. Many parameters driving solubility: complementarity, polarity, disruption of plane #ShefChem16 | |
| nathanbroo 'Solubility Cliffs' derived from matched molecular pairs. Cf. work from MedChemica #ShefChem16 | |
| WendyAnneW #ShefChem16 use CSD for lattice energy | |
| nathanbroo Use @ccdc_cambridge Access Structures to look at structures in 3D https://summary.ccdc.cam.ac.uk/structure-summary-form #ShefChem16 | |
| dgelemi @WendyAnneWarr #ShefChem16 website available to get info. Can be used without own crystal to get data @ccdc_cambridge | |
| WendyAnneW #ShefChem16 Beth Thomas mapping interactions around C=O using isostar. Isostar methodology explained | |
| stewartadc Play along with Beth's talk by entering the refcode at https://summary.ccdc.cam.ac.uk/structure-summary-form #shefchem16 | |
| WendyAnneW #ShefChem16 Thomas why do some cmpds have this lattice and some not. Stacking in ureas, notoriously insol cmpd | |
| WendyAnneW #ShefChem16 interaction map for benzamide. High density high MP | |
| nathanbroo Try CSD refcode: DPUREA for interactive 3D structure visualisation: https://summary.ccdc.cam.ac.uk/structure-summary-form #ShefChem16 pic.twitter.com/7yUokEyM3p | |
| WendyAnneW #ShefChem16 rufinamide JMC 59, 2346 | |
| WendyAnneW #ShefChem16 further down sol decision tree motif synergy can also affect soly JMC 59 , 1052 | |
| dgelemi #ShefChem16 example where increase logP >> increase solubility by disrupting crystal packing. Me addition on primary amide | |
| WendyAnneW #ShefChem16 steric bulk increasing soly. Related cmpds in CSD shown | |
| nathanbroo Disrupting planarity by introducing ortho twist but how much is enough? #ShefChem16 | |
| WendyAnneW #ShefChem16 packing disruption ortho twist example57 3719 JMC | |
| nathanbroo Disrupting planarity through ring saturation. Cyclopropyl example - cool little functional group (recent review @JMedChem) #ShefChem16 | |
| WendyAnneW #ShefChem16 tstlilb and vemluu cyclopropyl example JMC 58 130 | |
| WendyAnneW #ShefChem16 at bottom of decision tree , knowledge driven approach. MMP anal of CSD | |
| nathanbroo 'Isostructurality' introduced… Good title for a new book, perhaps… #ShefChem16 | |
| WendyAnneW #ShefChem16 CCDC the MMP research is a work in progress | |
| WendyAnneW #ShefChem16 MP and lipophilicity both designed in to our mols. Send her soly examples that are unusual | |
| nathanbroo Call for USOs @ccdc_cambridge 'Unexplained Solubility Outliers'. Example given is a simple regioisomer. #ShefChem16 | |
| WendyAnneW #ShefChem16 pic.twitter.com/GmLTcYqS8H | |
| pwk2013 #MedChem #CompChem #cheminformatics #ShefChem16 https://twitter.com/nathanbroon/status/749197360211890176 | |
| rguha kudos to @nathanbroon @baoilleach @WendyAnneWarr @dgelemi @mvkrier for great coverage of #shefchem16 | |
| dr_greg_la I wonder when the last time I stayed in student housing was. #allpartofthefun | |
| #ShefChem16 | |
| WendyAnneW #ShefChem16 first 3 papers after lunch will be tosco of cresset on diversity, leach on shape of mols, nicolaou on design of cmpd collectns | |
| WendyAnneW @dr_greg_landrum #ShefChem16 I stayed in Downing college Cambridge for CCDC 50th last year. Accommodation was excellent. Shef not so posh️ | |
| InfoSchool The 7th Joint Sheffield Conference on Chemoinformatics is today til Wed., organised by our academics! #ShefChem16 http://bit.ly/29qMUKN | |
| ZINClick Waiting for your talk Paolo! #ShefChem16 https://twitter.com/cressetgroup/status/749879294655131649 | |
| deniseOme .@nathanbroon tuned in #ShefChem16 to find out how @targetvalidate w/ info on genes, disease, drugs, pathways can help in #drug validation | |
| cressetgro Next up at #ShefChem16 is Paolo Tosco who will present on ‘Examining the Diversity of Large Collections of Building Blocks in 3D’ #CompChem | |
| dgelemi Time to restart #ShefChem16 missing plugs along seats to charge phone/tablet/laptop pic.twitter.com/IrhAoRqZ7m | |
| baoilleach #ShefChem16 (meta: batteries back up) | |
| baoilleach #ShefChem16 Paolo Tosco @cressetgroup on 3D diversity of building blocks | |
| baoilleach #ShefChem16 Comprehensive Fragment Library (CFL) - which fragments to include from large potential set? | |
| WendyAnneW Tosco's Leap to lead platform #ShefChem16 | |
| baoilleach #ShefChem16 3d similarity necessary or just 2d sufficient? Let's find out... | |
| WendyAnneW #ShefChem16 tosco pilot study to see if 3D similarity feasible. Bioblocks used | |
| nathanbroo Up next is Paulo Tosco @cressetgroup on the analysis of 3D building blocks. #ShefChem16 | |
| dgelemi #ShefChem16 3D clustering from @cressetgroup on 800k virtual library collection using shape and field, including diastereoisomers #bigtask | |
| WendyAnneW #ShefChem16 tosco diastereo enumeration pitfalls discussed | |
| baoilleach #ShefChem16 diastereomer enumeration - not all independent for ring systems - not all are actually stereocenters - nitrogen inversion pssble | |
| mvkrier #ShefChem16 Paolo Tosco from @cressetgroup describing a diastereoisomer generation worfklow | |
| WendyAnneW #ShefChem16 accelrys draw and RDkit compared. Used latter. Redo coords after enumeration to see which stereoisomers feasible | |
| baoilleach #ShefChem16 try to generate diastereomers and discard those not possible in 3D, also those with high E | |
| baoilleach #ShefChem16 XED force field - eXtended Electron Distribution - multipoles are modelled via additional monopoles with dummy atoms VinterJCAMD | |
| WendyAnneW Jcamd 1994 ref for XED force field #ShefChem16 | |
| WendyAnneW #ShefChem16 XED ft gives detailed interaction patterns | |
| baoilleach #ShefChem16 Force field gives detailed electrostatic interaction patterns - matches exptal much better than traditional FFs | |
| baoilleach #ShefChem16 Molecular interaction fields (MIFS) contain too much info to be used computationally quickly so compress down to a few points | |
| baoilleach #ShefChem16 Whole MIF used for sim measure but field points used in first steps | |
| dr_greg_la Collecting @RDKit_org bugs during Paolo Tosco's talk! I love it when people really push the code. #ShefChem16 | |
| WendyAnneW #ShefChem16 cresset MIFs contain too much info. So condense to fewer field points. But 3D similarity uses all field | |
| baoilleach #ShefChem16 Sim measured as a combination of shape (0.25) and fields (0.75) | |
| WendyAnneW #ShefChem16 pair wise similarity assessment assesses 25 % shape and 75% field | |
| baoilleach #ShefChem16 3h for pairwise ECFP4 similarity; 250 CPU days for shape sim! 20k x 20k molecules | |
| WendyAnneW #ShefChem16 20k diverse cmpds used in pilot took 96 cpu days | |
| baoilleach #ShefChem16 2D vs 3D correlation: very little correlation - this is expected for molecules with low similarity | |
| dgelemi #ShefChem16 no correlation at all between 2D ecfp4 and field 3D similarity #expected | |
| WendyAnneW #ShefChem16 sim value distributions better for 3D than 2D. no correlation between the two sets of structures | |
| baoilleach #ShefChem16 data clustered with k-medoids/CLARANS algorithm (in-house C++) - cluster tightness assessed by silhouette metric | |
| WendyAnneW #ShefChem16 tosco used silhouette metric to... | |
| WendyAnneW #ShefChem16 tosco now showing clustering examples pic.twitter.com/o1xbxOgZJF | |
| baoilleach #ShefChem16 2D data disastereomers point in different directions when considered in 3D, but using field+shape everything lines up better | |
| WendyAnneW #ShefChem16 3D fields and shapes method gives mols with much better fields than 2D ecfp4 | |
| baoilleach #ShefChem16 advantages of 3D - finds similarities across diastereomers and confs | |
| WendyAnneW #ShefChem16 full set of 800k cmpds not feasible so used diverse 150k to do the clusters | |
| baoilleach #ShefChem16 full set of 750K would take too long - chose diverse set of 150K. 5K CPU days. Used compressed files to reduce I/O contention. | |
| dgelemi #ShefChem16 need better CPUs. 2 months calculation on 2k CPU! Need strategy to take number down. Cluster diverse set then assigned the rest | |
| baoilleach #ShefChem16 First mention of cloudy cloud - Amazon EC2 | |
| WendyAnneW #ShefChem16 tosco discusses his job distribution strategy (nodes etc.) Amazon elastic cloud | |
| WendyAnneW #ShefChem16 tosco discusses hec cloud infrastructure | |
| baoilleach #ShefChem16 One master m3.large on demand, 64 c3.8xlarge spot instances, NFS-mounted EBS, 2048 jobs in parallel | |
| WendyAnneW #ShefChem16 clustering took 4 weeks | |
| baoilleach #ShefChem16 Took 4 days in the end. Clustering then took 4 weeks. | |
| dgelemi #ShefChem16 cluster with large number of nodes and flexibility using EC2 from @amazonpic.twitter.com/SnwULAeBAL | |
| dr_greg_la Paolo's also providing a nice intro to some AWS features. Good to see this in a #cheminformatics talk | |
| #ShefChem16 | |
| WendyAnneW #ShefChem16 silhouette very similar to what he would have got using all 800k cmpds | |
| nathanbroo Large number of comparisons taking many thousands of CPU hours cluster 3D building blocks. Tosco #ShefChem16 | |
| WendyAnneW #ShefChem16 all cmpds in 800k set now linked to ... | |
| baoilleach #ShefChem16 This was a collab with BioClocks. Thanks to #rdkit too which was used throughout. | |
| baoilleach #ShefChem16 (type: BioBlocks) | |
| nathanbroo Now sat on the floor at the back of the room as @georgeisyourman stole my seat. #ShefChem16 | |
| drjohndhol A good crowd in at #ShefChem16 pic.twitter.com/PoWWDXRxLs | |
| baoilleach @georgeisyourman #ShefChem16 Correction - first mention of cloudy cloud in a non-plenary :-) | |
| baoilleach #ShefChem16 Andrew Leach - yes that one - no, the other one - on impact of shape on its bio and phy props | |
| WendyAnneW #ShefChem16 pic.twitter.com/6ZwN7Wbhzc | |
| baoilleach #ShefChem16 no matched molecular pairs will be mentioned | |
| WendyAnneW #ShefChem16 leach. Why shape is important | |
| baoilleach #ShefChem16 Fischer + Pauling lock-in-key - shape is important | |
| nathanbroo Next up: Andrew Leach on molecular shape and the importance in biological activity. #ShefChem16 @pwk2013pic.twitter.com/YwB7CDxmM2 | |
| WendyAnneW #ShefChem16 lock and key of course | |
| WendyAnneW #ShefChem16 leach. Isosteres are important in meds hem | |
| baoilleach #ShefChem16 Isosteres - "iso" + "stereoes" - equal solid/hard | |
| baoilleach #ShefChem16 Example: cimetidine->ranitidine/nizatidine - ranitidine built the Stevenage cite for GSK (undergrad rumour :-) | |
| WendyAnneW #ShefChem16 shape has strong influence on pharm props | |
| baoilleach #ShefChem16 To test for "shape": look at enantiomers - if shape unimportant then should have same props | |
| dgelemi #ShefChem16 Andrew leach on shape. One case study are enantiomers where shape influence can be tested on pharmaceutical properties | |
| baoilleach #ShefChem16 Looking at pairs of enantiomers (enants from now on) - same functional groups arranged differently in space | |
| WendyAnneW #ShefChem16 bl...y type ahead. I meant medchem not mend hem or meds chem. Think I'll stick to photos | |
| baoilleach @georgeisyourman #ShefChem16 'sokay - I'm presenting the plenary poster | |
| baoilleach #ShefChem16 Leach et al. MedChemComm 2012 is all about this | |
| WendyAnneW #ShefChem16 Leach medchemcomm 2012 3 528 | |
| mvkrier #ShefChem16 Andrew G. Leach from @LJMU takes up the cudgels for enantiomers and their difference in properties | |
| cressetgro See Paolo Tosco's presentation from #ShefChem16 http://bit.ly/CresBB3D #BuildingBlocksIn3D #CompChem #rdkit | |
| baoilleach #ShefChem16 Gleeson binned data awakening the ghost of @pwk2013 | |
| WendyAnneW #ShefChem16 pic.twitter.com/u2zQlglQX4 | |
| baoilleach #ShefChem16 "shape" has improved the lives of many people, and pharma CEOs too | |
| nathanbroo Shape influences biological recognition. Important influence on pharmaceutical properties. Enantiomers used as case study. #ShefChem16 | |
| baoilleach #ShefChem16 a quiz!! help me internets | |
| baoilleach #ShefChem16 what shape is this? (shows a cube) | |
| baoilleach #ShefChem16 what shape is this? (shows a molecular shape) | |
| GJPvWesten @baoilleach #ShefChem16 did you get it right? ;) | |
| WendyAnneW #ShefChem16 shape of vancomycin is v important but how do we talk about it? | |
| nathanbroo Shapes are important but how do we process them and what is the recognition process? #ShefChem16 pic.twitter.com/QS4FtnSk0X | |
| mvkrier #shefchem16 as my greek medchem colleague always says: without chirality, there is no life | |
| baoilleach #ShefChem16 What shape is this? (shows an interesting carrot) | |
| WendyAnneW #ShefChem16 speaks for itself... pic.twitter.com/fiBYOfpYk6 | |
| baoilleach #ShefChem16 ROCS @OpenEyeSoftware can measure shape sim via atom-centered Gaussians | |
| dgelemi #ShefChem16 waiting to see if @WendyAnneWarr photo of the slide is going to pass NSW filter from Twitter... | |
| WendyAnneW #ShefChem16 ROCS is the answer JCIM 2005, 45, 673 | |
| baoilleach #ShefChem16 Need reference shapes. Haigh/Pickup/Grant/Nicholls - shape Tanimoto - diversity selection of dataset | |
| WendyAnneW #ShefChem16 leach uses shape tanimoto and ROCS | |
| baoilleach #ShefChem16 Low shape Tanimotos are in the noise so best to use a cutoff and only consider as similar if above cutoff | |
| WendyAnneW #ShefChem16 noise can be a problem | |
| baoilleach #ShefChem16 Haigh et al compared shape fps to ROCS, but didn't link to measured props and work out what the fp comparisons really mean | |
| baoilleach #ShefChem16 We are interested in which set of ref shape is best, what do the fp Tanimotos really mean, and something else I missed | |
| baoilleach #ShefChem16 Ligand Expo was used to derive our Shape Database | |
| WendyAnneW #ShefChem16 leach used Ligand Expo to derive his shape database. Test set of robin Taylor et al | |
| baoilleach #ShefChem16 Taylor et al JCAMD 2012 dataset of bio activity where molecules are known to share a binding mode | |
| nathanbroo Is shape another way of looking at molecular complexity? #ShefChem16 | |
| baoilleach #ShefChem16 Compare sim of molecules that bind to same vs different targets - logistic regression | |
| WendyAnneW #ShefChem16 leach results pic.twitter.com/axjYHopmUO | |
| baoilleach #ShefChem16 (@georgeisyourman sees neighbourhood behaviour behind every graph) | |
| dgelemi #ShefChem16 complex graphs there. 2 histograms (up and down the plot) plus a line for ROC #overcrowded | |
| baoilleach #ShefChem16 Our calculations use only shape. Comparisons of docking and shape isn't really like-with-like. | |
| WendyAnneW #ShefChem16 not comparing like with like. Only using shape | |
| baoilleach #ShefChem16 There is a remarkable ability to identify molecules with shared bioactivity when ONLY shape is considered | |
| baoilleach #ShefChem16 All sets of reference shapes will be made available | |
| baoilleach #ShefChem16 ...switching to solubility... | |
| WendyAnneW #ShefChem16 pic.twitter.com/g90KuMcATA | |
| baoilleach #ShefChem16 Lists 20 references on screen with solubility data | |
| WendyAnneW #ShefChem16 leach. And now apply this to solubility | |
| baoilleach #ShefChem16 About 5600 compounds with solubility and mp data - divided in training/test - built models with only SlogP | |
| WendyAnneW #ShefChem16 models built with slogp & slogp plus MP and slogp plus GPs | |
| baoilleach #ShefChem16 R-squared in 0.6 range even for training. | |
| nathanbroo Now onto solubility (again!). Data collated from many sources. Models using fingerprints & ClogP seem better than MP & ClogP. #ShefChem16 | |
| baoilleach #ShefChem16 Some influential shapes. (I've missed the connection to solubility here - oops) | |
| WendyAnneW #ShefChem16 leach shows some influential shapes | |
| dgelemi @baoilleach #ShefChem16 similar to USR from @pjballester http://www.ncbi.nlm.nih.gov/pubmed/17342716 with shape similarity | |
| WendyAnneW #ShefChem16 sigh. Slogp plus fingerprints not gps | |
| baoilleach #ShefChem16 Dedication to Andy Grant who spurred his interest in this area | |
| baoilleach @pwk2013 #ShefChem16 (ghostly present here at the conference) | |
| baoilleach #ShefChem16 Christos Nicolau on ...Designing compound collections for success | |
| nathanbroo Next up (my some-time co-author), Christos Nicolaou from Lilly on designing compound collections. #ShefChem16 | |
| baoilleach #ShefChem16 Has previously spoken on Proximal Lilly Collection (PLC). What's new? | |
| dgelemi #ShefChem16 Nicolaou started his talk to say that it's similar to one 3 yrs ago. What's new? Now they have more data and use in real life | |
| baoilleach #ShefChem16 sees drug discov as a computational optimization - multiple objectives - need effective search of chemical space | |
| mvkrier #ShefChem16 Christos A. Nicolaou from @LillyPad will tell us about Proximal Lilly Collection in practice for 3 years | |
| nathanbroo Nicolaou almost using the Douglas Adams quote that [chemistry] space is big! Really, really big! #ShefChem16 | |
| GJPvWesten @drjohndholliday But picked up several followers already... #ShefChem16 | |
| baoilleach #ShefChem16 number of atoms on earth (10^50) is less than the size of the small molecule universe (10^60) | |
| nathanbroo Nicolaou using mass of the earth as metaphor for size of chemistry space. I use a double decker bus. #ShefChem16 | |
| WendyAnneW #ShefChem16 people calculate the number of atoms on earth. Think how long to make all mols in chem space | |
| baoilleach #ShefChem16 Working on enabling the generation of large virtual libs and real collections, and methods to probe those spaces. | |
| WendyAnneW #ShefChem16 the general approach to exploring space is search based. Lilly's focus is interestingness not just bigness | |
| baoilleach #ShefChem16 What about interestingness? He doesn't think enough work has been done on this - partly because of size involved | |
| baoilleach #ShefChem16 PLC - building blocks, virtual synthesis via rules -> large number of compounds | |
| baoilleach #ShefChem16 see also BI-Claim, PGVL, PLC, Scubidoo, SAVI (Marc's talk earlier) | |
| WendyAnneW #ShefChem16 proximal Lilly collection published in JCIM. Bi claim pgvl and other work acknowledged | |
| baoilleach #ShefChem16 multi-step DNA encoded library (DEL) via multi-component reaction | |
| baoilleach #ShefChem16 combinatorial chemistry on steroids | |
| WendyAnneW #ShefChem16 DNA encoded library (DEL) technology overview | |
| GJPvWesten #ShefChem16 DNA-Encoded library by Christos Nicolaou, million cmpds in a testtube.. cool! | |
| nathanbroo DNA-encoded libraries and characteristics of the new chemical space that is opened up. #ShefChem16 | |
| baoilleach #ShefChem16 DNA tag added that identifies compound. Mixture of 1 million potentially. Can screen, and work out the DNA codes for hits. | |
| baoilleach #ShefChem16 Can used a ltd no of robust rxns, or finite no of building blocks, full enumeration. | |
| dgelemi #ShefChem16 DEL has limited BB and number of reactions but still huge numbers. Also full enumeration. But is this space interesting? | |
| dr_greg_la Christos keeps telling us large chem space is automatically interesting. I'm waiting for the "but" | |
| #ShefChem16 | |
| baoilleach #ShefChem16 Took some of these sets and analysed them for "interestingness" | |
| WendyAnneW #ShefChem16 100 X 500 X 500 combinatorialI library. Some people are actually trying to make these huge spaces. He does not try this | |
| baoilleach #ShefChem16 intra-collection diversity, property values, ... | |
| baoilleach #ShefChem16 Compare real collections (e.g. subset of PubChem, Lilly), and virtual collections | |
| baoilleach #ShefChem16 Used #rdkit for analysis via @knime | |
| baoilleach #ShefChem16 looking at intraset near-neighbour distance (paper on the way explaining all this) | |
| WendyAnneW #ShefChem16 nicolaou results pic.twitter.com/lBWIgawbI1 | |
| baoilleach #ShefChem16 DEL sets have larger no of compds per cluster, but fluctuates largely | |
| baoilleach #ShefChem16 C80% - how compounds would you need to screen to ensure that finds a nbr within 0.8 | |
| baoilleach @dgelemi #ShefChem16 @WendyAnneWarr 10 min left on battery again!! | |
| dgelemi #ShefChem16 there are DELs and there are DELs. Not all libraries are equal even with same technology (large space A != large space B) | |
| WendyAnneW #ShefChem16 diversity analysis . How many cmpds need to be compared worse for PLC than for DEL | |
| baoilleach #ShefChem16 DEL libs are highly homogeneous, explores rel few compound classes | |
| baoilleach #ShefChem16 gc3tk used ?? to do analysis | |
| WendyAnneW #ShefChem16 DEL designs differ a lot. Which to make first? Merck atom pair FPs and c80% diversity anal. NN distributn & other approaches | |
| WendyAnneW #ShefChem16 they work within diversity neighbourhoods eg drugs drugs, frags frags | |
| mvkrier #shefchem16 How does your chemical diversity neigbourhood look like (A. Nicolaou) | |
| WendyAnneW #ShefChem16 and now for tea - recharge our batteries, in all respects | |
| nathanbroo Time for coffee! Very much needed! #ShefChem16 | |
| rguha @baoilleach how is interestingness being defined? #ShefChem16 | |
| dgelemi #ShefChem16 poster session coming ... Or seating next to the wall to charge phone. Another good session. | |
| WendyAnneW #ShefChem16 Paul Hawkins of Open Eye talks about SNOWFLAKE. Data driven decisions in lead discovery | |
| nathanbroo Up next: Paul Hawkins @OpenEyeSoftware on SNOWFLAKE: data-driven decisions in lead discovery #ShefChem16 | |
| dgelemi #ShefChem16 session to start. 1st talk from Hawkins from #openeye. Snowflake with abstract containing cloud and GPUs | |
| baoilleach #ShefChem16 Paul Hawkins @OpenEyeSoftware on Snowflake - data-driven decisions in lead discov | |
| baoilleach #ShefChem16 we've been analysing VS results wrong | |
| baoilleach #ShefChem16 VS about separating scores - decoys vs actives | |
| nathanbroo Combative start: we're analysing virtual screening results in he wrong way. #ShefChem16 | |
| baoilleach #ShefChem16 Talking about ROCS scores | |
| mvkrier #Shefchem16 Ready for Paul Hawkins from @OpenEyeSoftware; snowflake, hope it doesn't develop into a snowstorm | |
| baoilleach #ShefChem16 No more sampling to infer population - because of cloudy cloud and GPU | |
| mvkrier the cloud and the gpu=> no more sampling (P. Hawkins) #ShefChem16 | |
| WendyAnneW #ShefChem16 vhts work by separating score distributions. He uses ROCS shape similarity but his talk applies to other s/w too. Probability | |
| WendyAnneW #ShefChem16 Hawkins. No need for sampling any more. Cloud and clusters can be used | |
| baoilleach #ShefChem16 10^7 molecules: ROCS 60-90 mins over 1200 CPUs, FastROCS (2 GPUs): 1-2 mins | |
| WendyAnneW #ShefChem16 distributions for ALL mols. | |
| baoilleach #ShefChem16 "The cutoff" - based on scores, or rank/number - what is the right cutoff? | |
| nathanbroo "What's the perfect cut-off for ROCS? There isn't one. It depends…" #ShefChem16 | |
| dgelemi #ShefChem16 "what's the best cutoff from VS", best answer is "it depends" @OpenEyeSoftware | |
| WendyAnneW #ShefChem16 analysing a virtual screen you used to set a cutoff. You do not Need to. | |
| baoilleach #ShefChem16 Cutoff should be based on the results, not set in advance.... | |
| nathanbroo Virtual screening cut-offs: we still have to do something. What are the options? #ShefChem16 | |
| baoilleach #ShefChem16 The cutoff decides precision vs recall (signal-to-noise) - Goldilocks "just about right" cutoff | |
| nathanbroo The #JustAboutRight Goldilocks cut-off. #ShefChem16 | |
| WendyAnneW #ShefChem16 cutoff means too much noise or too little signal. He recommends the just about right cutoff. The goldilocks cutoff | |
| baoilleach #ShefChem16 Example: 9m molecules from eMolecules, 96 queries from DUDE, and ROCS score distribs | |
| dgelemi #ShefChem16 presentation from Hawkins available here https://openeye.box.com/s/g6kmw8kc3ey1c3uoo3g64d2b131qxgz1 @OpenEyeSoftware for our Twitter friend | |
| baoilleach #ShefChem16 The query affects the distribution of scores. FIxed score cutoffs are a bad idea. | |
| nathanbroo "Fixed score cut-offs are a bad idea" no disagreement there. Does anyone still do that? #ShefChem16 | |
| WendyAnneW #ShefChem16 96 queries from DUDE database.get ROCS score distributions. Fixed score cutoff are a bad idea. Mean distributn differs by query | |
| baoilleach @neysanev #ShefChem16 Mean score @nathanbroon @WendyAnneWarr @dgelemi We each have a unique spin on it. | |
| rguha you could also consider cutoff in terms of resource availability (take as many as you can follow up)? #shefchem16 https://twitter.com/baoilleach/status/749983971535097856 | |
| nathanbroo Rank scoring also a bad idea… #ShefChem16 | |
| baoilleach #ShefChem16 Snowflake ref from Fight Club "you are not a snowflake" | |
| pjballeste @dgelemi @baoilleach #ShefChem16 Now easy to use USR/USRCAT prospectively: http://nar.oxfordjournals.org/content/early/2016/04/22/nar.gkw320.abstract e.g.w/cytarabine: http://usr.marseille.inserm.fr/iview/?575581c69641d65b5da98dab | |
| WendyAnneW #ShefChem16 score distributions in lb virtual screening are unique for the population | |
| nathanbroo "Virtual screening is a beautiful and unique snowflake" (Palahnuik) Must be treated differently every time #ShefChem16 | |
| baoilleach #ShefChem16 pessimism in analysing VS, e.g. if scoring is not working, are random; if no actives present | |
| WendyAnneW #ShefChem16 Hawkins. Pessimism in analysing VS? Database scores are distributed randomly. | |
| baoilleach #ShefChem16 Background distribution is considered to be normal (central limit theorem) | |
| WendyAnneW #ShefChem16 the population score is accessible | |
| baoilleach #ShefChem16 assumptions: deviations from normality indicate actives | |
| nathanbroo Snowflake assumptions: scale, scores & significance. Access entire population, one normal distribution #ShefChem16 | |
| baoilleach #ShefChem16 normal distrib is "unbounded" - sits on real number line | |
| baoilleach #ShefChem16 real scores are bounded (0->1, etc.) | |
| nathanbroo Fitting to a normal *not* calculating mean and standard deviation. #ShefChem16 | |
| mvkrier #shefchem16 Miss Smilla's Feeling for Snow to be applied for #virtual_screening | |
| baoilleach #ShefChem16 ...so unbound it "logit p = log(p/1-p)" logit transformation | |
| WendyAnneW #ShefChem16 deviations from normal at high scoring end found by snowflake | |
| baoilleach #ShefChem16 can we decide whether a VS works or not before exptal testing? | |
| dgelemi @WendyAnneWarr and used to know if a VS did work or not using statistics, and comes QQ plot. #ShefChem16 | |
| baoilleach #ShefChem16 QQ plot - want to see more high scoring molecules than expected by chance - comparison to normal | |
| baoilleach #ShefChem16 how to locate the Goldilocks cutoff - don't just stare at the top of the list | |
| WendyAnneW #ShefChem16 looking just at the top of the hit list is not the way to get the cutoff. Look at all the results | |
| baoilleach #ShefChem16 look at the whole list, and fit to normal distribution, and think about z-scores | |
| nathanbroo "Looking at just the top of the hitlist is uninformative." Paul Hawkins #ShefChem16 | |
| baoilleach #ShefChem16 what no of molecules do you expect at a particular z-score? | |
| baoilleach #ShefChem16 Consider the actives as a small Gaussian sitting on top of a large background Gaussian | |
| georgeisyo @GJPvWesten Z-scores #ShefChem16 | |
| baoilleach #ShefChem16 Binomial test: what's the chance that the selected cutoff is valid | |
| WendyAnneW #ShefChem16 binomial test for probability of success in goldilocks cutoff (GC) | |
| baoilleach #ShefChem16 Think about probabilities instead of scores; the relevant probability of success | |
| WendyAnneW #ShefChem16 the language of odds is easier for the chemist to understand. Score to z score to odds | |
| baoilleach #ShefChem16 z-score of 4: 1 in 31574, etc. | |
| baoilleach #ShefChem16 can turn molecule scores into z-scores - can tell if interesting vs uninteresting - select hits based on probabilities | |
| WendyAnneW #ShefChem16 you can find extremely unusual molecules - the ones to make | |
| nathanbroo Snowflake method for prioritising interesting hits. Paul Hawkins. #ShefChem16 pic.twitter.com/xxsoMLtqZy | |
| baoilleach #ShefChem16 CP3A4 example: no cutoff can be found - no actives are present, or at least no confidence that top results are better than rndom | |
| WendyAnneW #ShefChem16 examples. In one case no GC found . less than 90k to 1 chance of mols | |
| baoilleach #ShefChem16 XIAP: lines cross, and can find goldilocks cutoff. One in 3.5million chance scores. | |
| jwmay Anyone else reminded of this: http://www.daylight.com/meetings/emug97/Bradshaw/Significant_Similarity/Significant_Similarity.html #ShefChem16 | |
| WendyAnneW One in 3.5 million example next #ShefChem16 | |
| WendyAnneW #ShefChem16 third eg 4.2 million to one against | |
| baoilleach #ShefChem16 Easy to digest way of approaching significance in sim measures. Graphical. | |
| baoilleach #ShefChem16 Use all your hard-won data - the entire distrib. Treat each run as unique - diff cutoff. ...and st else | |
| cdsouthan . @WendyAnneWarr appreciating your #ShefChem16 tweets but some are a tad cryptic | |
| nathanbroo Use all the data: fit entire distribution. Treat each VS As unique. Talk about results in terms of odds *not* scores. #ShefChem16 | |
| nathanbroo Snowflake in summary. #ShefChem16 pic.twitter.com/nvNeXELz8V | |
| baoilleach @cdsouthan #ShefChem16 @WendyAnneWarr dude - this ain't easy, listening, understanding and typing soundbites :-) | |
| rguha in this case, wouldn't fitting a mixture model be more robust? (e.g. Tong et al http://dl.acm.org/citation.cfm?id=1234656) #shefchem16 https://twitter.com/baoilleach/status/749987098124517376 | |
| mvkrier #ShefChem16 acknowledgements goes to @mattgeballe , @craigbruce and Forrest York | |
| rguha @baoilleach what the 'entire distrib' or 'population' that is being referred to? The entire screening deck? #shefchem16 | |
| GJPvWesten @georgeisyourman #ShefChem16 loving them ;) | |
| WendyAnneW @baoilleach @cdsouthan #ShefChem16 I do better with my laptop... | |
| nathanbroo Up next: Yi Mok @ICR_London talking on EnCore a new method we have developed for scaffold morphing to enrich SAR #ShefChem16 | |
| baoilleach #ShefChem16 Yi Mok: ICR on enriching SAR info by scaffold enum | |
| GJPvWesten @georgeisyourman #ShefChem16 improved MCC from ~0.20 --> ~0.40 for MCNBC ... | |
| nathanbroo I'll probably step away from tweeting this one. Might be a bit biased! #ShefChem16 | |
| dgelemi #ShefChem16 next talk from @ICR_London Yi Mok on scaffold enumeration and clustering. Scaffold Tree and HTS on abstract | |
| baoilleach #ShefChem16 What is a mol scaffold? Part not changing within a cmpd series | |
| mvkrier #ShefChem16 Yi Mok from @ICR_London : reminding us of the different definitions of a molecular #scaffold | |
| baoilleach #ShefChem16 Defn preferably dataset independent, objective and invariant. Example: Scaffold Tree (Ansgar in audience!) | |
| cdsouthan . @baoilleach @WendyAnneWarr #ShefChem16 For us (envious) remotees there's no panic on the Titanic i.e. slower more cogent tweets better | |
| WendyAnneW #ShefChem16 scaffolds should be independent of dataset. E.g., Novartis scaffold tree | |
| baoilleach #ShefChem16 Such scaffolds can be used in clustering, represents "compd series", easily interpretable, may overlook key functional groups | |
| baoilleach #ShefChem16 introduce "controlled fuzziness" in scaffold representations | |
| dgelemi #ShefChem16 not science related but my neighbour is doing an art like drawing. I'm impressed. #scribbling on notepad | |
| WendyAnneW #ShefChem16 clustering using core scaffolds, objective scaffolds. Good 4 SAR and interprets but may be 2 stringent or miss key funct groups | |
| baoilleach #ShefChem16 EnCore: Enumeration of Core Scaffold. Mimic scaffold exploration efforts and enrich SAR info during HTS hit id | |
| WendyAnneW #ShefChem16 EnCore. Mok and brown JCIM submitted | |
| baoilleach #ShefChem16 (aside: didn't they just make this name up last week?? @nathanbroon :-) | |
| dgelemi #ShefChem16 paper from Mok's presentation, EnCore submitted in JCIM with @nathanbroon Soon in your RSS alert | |
| baoilleach #ShefChem16 Features: CNO elemental changes (mutation), preserve aromaticity, keep unique mutated scaffolds-->enumerated scaffold cluster | |
| baoilleach #ShefChem16 ...several generations, iterative | |
| WendyAnneW #ShefChem16 C N O changes. Mutation on scaffold once per generation. Keep aromaticy From mutated scaffolds get enumerated scaffold cluster | |
| baoilleach #ShefChem16 Example: imidazole as parent scaffold. 1st gen gives 6 mutated scaffolds, 2nd gen gives an additional 8 | |
| baoilleach #ShefChem16 Relevant to med chem? Let's find out... | |
| baoilleach #ShefChem16 Can the scaffold exploration recover explored scaffolds in a med chem compound series? | |
| WendyAnneW #ShefChem16 can the scaffolds retrieve scaffolds explored in medchem literature? | |
| baoilleach #ShefChem16 Mok & Brown, JCIM, submitted | |
| nathanbroo @baoilleach @WendyAnneWarr the anticipation is palpable! #ShefChem16 | |
| baoilleach #ShefChem16 Looking at all 62 publications with at least 100 compounds with IC50. After filters --> 43 med chem series. Variety of targets | |
| WendyAnneW #ShefChem16 selection of JMC series made. At least 100 cmpds with ic50. Series for wide spectrum of targets. | |
| nathanbroo 43 medicinal chemistry series defined from @ChEMBL in submitted manuscript. #ShefChem16 | |
| baoilleach #ShefChem16 The top level 1 scaffold in each series was used for EnCore enumeration | |
| baoilleach #ShefChem16 20 of the series were found at the Level 1, 10 found at Level 2, the few remaing at 3/4 | |
| baoilleach #ShefChem16 (meta: need to sort out battery again! Firefox/tweetdeck is not light) | |
| WendyAnneW #ShefChem16 his scaffolds ARE relevant. 20 scaffolds at first generation, 9 more at second, 2 more at third. EnCore mimics the medchem | |
| WendyAnneW #ShefChem16 how many generations best? | |
| baoilleach #ShefChem16 DrugBank approved drugs contain 475 unique level 1 scaffolds. | |
| baoilleach #ShefChem16 Two rings connected by very long scaffold leads to many enumerated scaffolds. | |
| WendyAnneW #ShefChem16 used DrugBank to test this. Lipin ski like cmpds. 475 unique level 1 scaffolds | |
| baoilleach #ShefChem16 (meta: sitting close to a plug at the back now, but could do with a binoculars) | |
| WendyAnneW #ShefChem16 average epfp7 Structural similarity studied. tanimoto below 6 by third or fourth generation | |
| baoilleach #ShefChem16 Can EnCore associate structurally related screening cmpds to the parent scaffold? | |
| WendyAnneW #ShefChem16 now some work on overlapping series, | |
| baoilleach #ShefChem16 HTS screening set: ~0.25m -> 23K Level 1 scaffolds. | |
| baoilleach #ShefChem16 After two gens of mutations, 74% of scaffold clusters found. Enrichment achieved! | |
| georgeisyo @nathanbroon I will count this as the first mention of @chembl data at #ShefChem16 | |
| deniseOme .@baoilleach Wonder if these targets are associated with diseases in the #targetvalidation platform https://www.targetvalidation.org/ #ShefChem16 | |
| dgelemi #ShefChem16 sent to the past by listening to the talk from Y Mok when we were both at @UoDLifeSciences | |
| WendyAnneW Can enrich SAR by using his enumerated scaffolds #ShefChem16 | |
| baoilleach #ShefChem16 Looking at singletons originally, but after enumeration can find many screening cmpds. | |
| baoilleach @deniseOme #ShefChem16 Pinging @nathanbroon | |
| DrJoshuaBo The DUDE abides #ShefChem16 | |
| baoilleach #ShefChem16 I should read this paper when it comes out. | |
| WendyAnneW @baoilleach #ShefChem16 ten thousand compounds in one case | |
| DrJoshuaBo We are all special, beautiful and unique snowflakes #ShefChem16 | |
| WendyAnneW #ShefChem16 now Peter Kolb winner of MGMS prize. Exploring chemical and GPCR ligand space | |
| nathanbroo Next up: Peter Kolb - recipient of the MGMS Silver Jubilee with his award lecture. #ShefChem16 | |
| baoilleach #ShefChem16 Peter Kolb - In-silico exploration of chemical and GPCR ligand space | |
| baoilleach #ShefChem16 Talking about docking, GPCRs and kinases, and method dev | |
| mvkrier #ShefChem16 @MgmsUpdates MGMS Lecture from Peter Kolb from @Uni_MR | |
| DrJoshuaBo Yi Mok cookin raw with the Brooklyn boy #EnCore #ShefChem16 | |
| nathanbroo Docking is key technique applied in the Kolb lab. Now their applications in GPCRs. #ShefChem16 | |
| dgelemi #ShefChem16 start of the presentation with acknowledgement and telling that it's not going to be a method talk. GPCR, docking, MD on the way | |
| baoilleach #ShefChem16 Docking good because can deal with orphan targets, novel chemotypes, hit rates of 1% (HTS is only 0.1%) | |
| baoilleach #ShefChem16 How GPCRs work. G-protein, beta arrestin. rhodopsin first to be xtallised. Since 2007 many more. | |
| WendyAnneW #ShefChem16 intro. advantages of docking. Gpcrs transmit signals | |
| GJPvWesten #ShefChem16 Beta2, sexy binding site :) | |
| DrJoshuaBo If a dog barks at me I get a dopamine rush but I get what you're saying Pete! #ShefChem16 | |
| baoilleach #ShefChem16 B2AR. Nice binding pocket. Like Bordeaux wine glass. 5 known inverse agonists. | |
| WendyAnneW #ShefChem16 inverse agonists of beta 2 AR. Five known | |
| baoilleach #ShefChem16 Kolb PNAS 2009 - found 6 compds from 25 tested. Six potent and novel hits. | |
| WendyAnneW #ShefChem16 Kolb found six potent and novel hits pnas 2009, 106, 6843 | |
| baoilleach #ShefChem16 Some of the hits look like expected, but novel effects also found. Have since been expanded upon. Very encouraging for docking. | |
| WendyAnneW #ShefChem16 so docking to gpcrs is a worthwhile endeavour | |
| baoilleach #ShefChem16 We can also identify fragment like binders, typically thought to be more difficult. | |
| WendyAnneW #ShefChem16 can also identify fragmentlike binders | |
| baoilleach #ShefChem16 Can we find molecules with tailored selectivity: CXCR3 vs CXCR4 | |
| jwmay Nice talk but can't help but wonder which tools makes 'OH' red and white? #ShefChem16 pic.twitter.com/pfeeA7NCLF | |
| WendyAnneW #ShefChem16 selectivity cxcr3 versus 4 investigated by docking | |
| baoilleach #ShefChem16 3m molecules docked to both receptors. Exptally dual binders are not described in ChEMBL - diff props. | |
| WendyAnneW #ShefChem16 homologous modelling was necessary | |
| WendyAnneW #ShefChem16 homology not homologous. Sigh | |
| baoilleach #ShefChem16 17 predictions, and all three selectivity groups were 50% correct (i.e. only CXCR3, only 4, both) | |
| dgelemi #ShefChem16 finding dual binder difficult. That's my daily job at @exscientialtd for GPCR and across gene family. It's possible | |
| MgmsUpdate Peter Kolb, winner of the 8th MGMS Silver Jubilee Award, speaking at #ShefChem16 on his docking work against GPCRs pic.twitter.com/1SbwHd6DhK | |
| WendyAnneW #ShefChem16 different binding mode for a significant compound. Dual binder | |
| baoilleach #ShefChem16 Different binding mode (exptally) for a dual binder. | |
| baoilleach #ShefChem16 Checked for non-specific inhibition. Detergent, spinning, dynamic light scattering, screening on unrelated target. | |
| WendyAnneW #ShefChem16 proved that the binding is "real" | |
| baoilleach #ShefChem16 suvorexant has unusual shape - horseshoe shape in xtal structure. Could we have found it? Yes. And other tool compds too. | |
| WendyAnneW #ShefChem16 he was able to reproduce suvorexant binding. Almorexant has intro I stacking | |
| baoilleach #ShefChem16 K003JG022 used as id for molecule - almost a WIsswesser Line Notation in itself! | |
| WendyAnneW #ShefChem16 sorry - hit send key too soon in last tweet. Rubbish... | |
| WendyAnneW #ShefChem16 docking can find novel chemotypes JCIM 2015, 55, 1824 | |
| baoilleach #ShefChem16 Chevillard JCIM 2015 - building blocks, 58 robust reactions -> 21m - SCUBIDOO | |
| nathanbroo It's not just me that uses Lego in slides on enumerating chemistry space! This one is rather good! #ShefChem16 pic.twitter.com/il1uMdA6Zy | |
| baoilleach #ShefChem16 stratified balanced sampling to select sample (aside: why not random?) | |
| WendyAnneW #ShefChem16 SCUBIDOO described in that JCIM paper | |
| baoilleach #ShefChem16 SCUBIDOO useful for finding tool cmpds, virtual screening. Available online. http://kolblab.org/scubidoo | |
| baoilleach #ShefChem16 Traffic light system used for showing hits: red->caution, might polymerise. | |
| nathanbroo SCUBIDOO: Screenable Chemical Universe - http://www.kolblab.org/scubidoo #ShefChem16 pic.twitter.com/p46LxMxxYH | |
| baoilleach @WendyAnneWarr #ShefChem16 You've called my bluff! | |
| baoilleach #ShefChem16 Overall impression is that Kolb is a GPCR inhibitor finding machine. Or his software is. | |
| nathanbroo .@baoilleach @WendyAnneWarr I mentioned them in my new book. #ShefChem16 | |
| WendyAnneW #ShefChem16 fragment built up and compatible building blocks found to get ligands for beta 2 AR binding site | |
| baoilleach #ShefChem16 Collab with Taros (?) in Dortmund, testing 240 molecules for b2AR. Need to ask for details - hot off the press... | |
| WendyAnneW #ShefChem16 conclusion. docking is great for finding ligands for gpcrs | |
| nathanbroo Nice to see success stories on docking. It is useful but you need to be careful how and when you use it & modulate expectations. #ShefChem16 | |
| baoilleach #ShefChem16 Question from floor on viz inspection step, importance thereof. Kolb tries to select novel ones which would be game changing. | |
| baoilleach #ShefChem16 ...has also tried to use machine learning to chose them, like a chemist would, but not quite there yet. | |
| WendyAnneW @baoilleach #ShefChem16 yes Taros | |
| DrJoshuaBo I dream of a world where picking molecules gives you street cred #ShefChem16 | |
| nathanbroo @baoilleach @WendyAnneWarr maybe… #ShefChem16 #shamelessadvertisingpic.twitter.com/CQkUs7Vknr | |
| nathanbroo Now onto the @MgmsUpdates AGM. #ShefChem16 | |
| jwmay Quick break - maybe time for a quick read (Shef circa '84) #ShefChem16 pic.twitter.com/RiIZuNw1AY | |
| ssalentin Great Conference #ShefChem16 so far! :) Looking forward to present #PLIP tomorrow here in Sheffield. #protein #ligand #interactions | |
| dgelemi #ShefChem16 @DDU_Dundee is present! @aschreyer Credo's database mentioned as wellpic.twitter.com/OmxDUIOkMN | |
| nathanbroo Starting another day with a conference breakfast. #ShefChem16 pic.twitter.com/vW1bcz6t6I | |
| dgelemi #ShefChem16 9am start approaching and breakfast room getting less crowdy pic.twitter.com/RudiHLsHbg | |
| jwmay My Poster (21) for #ShefChem16: "Sketchy Sketches". http://www.slideshare.net/NextMoveSoftware/sketchy-sketches-hiding-chemistry-in-plain-sight?qid=b10cc5a4-9f47-4eb1-ad48-d6ae50d3f159&v=&b=&from_search=1 | |
| baoilleach #ShefChem16 Nadine Schneider talking about reaction chemistry space | |
| nathanbroo Up first: Nadine Schneider and back to the roots of chem(o)informatics from NIBR. #ShefChem16 | |
| baoilleach #ShefChem16 Syn of cmpds is one of the bottlenecks in drug discov | |
| baoilleach #ShefChem16 Bias to more easily accessible compounds and to a small set of common reactions | |
| WendyAnneW #ShefChem16 marine Schneider of Novartis. Why we care about reactions | |
| nathanbroo We know lots of molecules but do we know how to make them. New ways to design synthetic routes. #ShefChem16 | |
| WendyAnneW #ShefChem16 Schneider. And why we use patents | |
| baoilleach #ShefChem16 Why use patent data? Rich source, only small overlap 6% with compounds in literature, rxns are actually used in pharma | |
| baoilleach #ShefChem16 Text-mining can extract a lot of info from patents | |
| nathanbroo Text mining allows extraction of compounds, targets, bio-affinities, melting points, yields whole reaction schemes #ShefChem16 | |
| baoilleach #ShefChem16 Schneider et al JMC 2016 analysis of 40 years worth of patents | |
| WendyAnneW #ShefChem16 Schneider . Admittedly patent reactions are error prone | |
| dgelemi #ShefChem16 extracting reactions from patents, paper picked up be @Dereklowe lots of interest on this patent data from chemists | |
| WendyAnneW #ShefChem16 chemists are interested in the data. See C&EN and in the pipeline blog | |
| nathanbroo Here is the paper from Schneider et al. http://pubs.acs.org/doi/abs/10.1021/acs.jmedchem.6b00153 #ShefChem16 pic.twitter.com/5nsXxlWMa6 | |
| baoilleach #ShefChem16 Textmining - identify exptal section in patent - extract product (usually in title, but not always) - convert to chemical struct | |
| WendyAnneW #ShefChem16 mining patents. Extract product is easy. Getting the structure Is harder | |
| baoilleach #ShefChem16 Textmining: extract the remainder of the reaction. Shoutout for Daniel Lowe of @nextmovesoftware | |
| WendyAnneW #ShefChem16 Schneider cites Lowe PhD thesis | |
| baoilleach #ShefChem16 Reactions are noisy: cleanup required - crude sanity checks - standardisation & duplicate removal | |
| baoilleach #ShefChem16 Duplicates removed with canonical reaction smiles | |
| nathanbroo Reaction standardisation & duplicate removal first. Canonical SMILES, lexicographic sort, hashing function #ShefChem16 | |
| WendyAnneW #ShefChem16 the reactions need clean up, de duplicating standardised etc. Then do reaction mapping | |
| baoilleach #ShefChem16 reaction role assignment and atom-to-atom mapping - often have more atoms on left than on right | |
| nathanbroo Reaction role assignment & atom-to-atom mapping next: reactants, reagents & products #ShefChem16 | |
| baoilleach #ShefChem16 Which are reactants and which are reagents? Can do atom mapping - find corresponding atoms - uses MCS based approach | |
| baoilleach #ShefChem16 Atom mapping: many different algorithms - huge challenge is identifying the atoms and bonds in the rxn | |
| WendyAnneW #ShefChem16 Chen in WiRES cited on mapping | |
| baoilleach #ShefChem16 For many applications, the exact mapping is not required, just need to reaction role assignment | |
| nathanbroo Sub graph matching with changing atoms and invariants. New fingerprint method for reaction role assignment. #ShefChem16 | |
| baoilleach #ShefChem16 A new method for rxn role assignment without mapping, based on fingerprints | |
| WendyAnneW #ShefChem16 schneider presents new fp assignment method without using mapping | |
| nathanbroo Fingerprint is very local form of Morgan fingerprint (radius = 1) with simple topological characteristics. #ShefChem16 | |
| baoilleach #ShefChem16 Morgan FPs (radius 1); detailed FP versus scaffold FP (the latter only has atomic no and no bond types) | |
| dgelemi #ShefChem16 lots of different atom mapping methods, including reaction fingerprint by adapting ecfp | |
| WendyAnneW #ShefChem16 Schneider explains reaction role assignment | |
| baoilleach #ShefChem16 Using count-based fingerprints; combine reactant fps in different combinations; ignore irrelevant combinations using heuristic | |
| baoilleach #ShefChem16 Build reaction fps based on difference between fps for product and reactant combinations | |
| WendyAnneW #ShefChem16 she did not cite InfoChem JCIM paper overview on mapping | |
| nathanbroo Rogers & Hahn cited for Morgan fingerprint (or Extended-Connectivity Fingeprint) http://pubs.acs.org/doi/abs/10.1021/ci100050t #ShefChem16 pic.twitter.com/8kPeTCRm1T | |
| nathanbroo Reaction assignment made by new scoring function for difference fingerprints of reaction fingerprints. #ShefChem16 | |
| baoilleach #ShefChem16 Score each fp based on bits mapped and bits left over - based on both detailed and scaffold fp - gives which are the reactants | |
| WendyAnneW #ShefChem16 she says no datasets available. Has she seen the InfoChem set? | |
| baoilleach #ShefChem16 What to compare to? The patent reaction dataset of 1.3m unique rxns of which 62% are classified using SMIRKS by NameRxn | |
| WendyAnneW #ShefChem16 use of next move name run | |
| baoilleach #ShefChem16 Dataset 1: 683 unbalanced rxns from patents with 228 rxn types | |
| philbiggin Great afternoon of talks at the 7th Sheffield Cheminformatics Conference #ShefChem16 yesterday. Thanks again to all who made the MGMS AGM! | |
| nathanbroo Dataset construction to validate new assignment method. #ShefChem16 | |
| baoilleach #ShefChem16 Dataset 2: randomly 50k from patents, all of the atoms need to be atom-mapped and only including classified rxns | |
| WendyAnneW #ShefChem16 she also uses indigo toolkit | |
| baoilleach #ShefChem16 (First mention of indigo toolkit) | |
| WendyAnneW #ShefChem16 grr! Next move name rxn not name run | |
| baoilleach #ShefChem16 NameRxn works by applying a pattern to the LHS and check whether the result matches the RHS | |
| nathanbroo Quick summary of NameRxn @nmsoftware which is a classification-based atom mapping procedure. #ShefChem16 | |
| WendyAnneW #ShefChem16 indigo reaction mapping uses substructure based mapping | |
| baoilleach #ShefChem16 Indigo toolkit uses a substructure-based algorithm | |
| WendyAnneW #ShefChem16 Schneider : 87% of reactants correctly assigned | |
| baoilleach #ShefChem16 Typical failures first: assignment of correct reactant only possible knowing the rxn mechanism | |
| nathanbroo 87% correct assignments in first set of results. Most issues in oxidations. #ShefChem16 | |
| baoilleach #ShefChem16 Noisy unbalanced patent data may contain multiple rxns in one scheme | |
| baoilleach #ShefChem16 2 possible solns predicted for 1.5% of dataset | |
| baoilleach #ShefChem16 Dataset 2: 97% agreement with NameRxn results | |
| nathanbroo Results on second dataset gives 97% agreement on. 50k unique reactions from patents. Very fast to run. #ShefChem16 | |
| baoilleach #ShefChem16 96% agreement with Indigo TK results | |
| WendyAnneW #ShefChem16 better than indigo toolkit | |
| WendyAnneW #ShefChem16 fp method even handles subtle examples | |
| baoilleach #ShefChem16 FP-based method even gets subtle diffs between reactants - summary: it works well, no atom mapping required, efficient, robust | |
| nathanbroo Summary from Schneider: efficient and robust fingerprint-based methods as alternative to atom-to-atom mapping step. #ShefChem16 | |
| WendyAnneW #ShefChem16 the new method will be included in RDkit | |
| baoilleach #ShefChem16 New method sheds some light on unclassified rxns in patent dataset | |
| baoilleach #ShefChem16 RDKit UGM 26-28 Oct in Novartis, Basel - come along | |
| nathanbroo 5th #RDKit UGM to be held in Basel on 26-28th October. Free registration still available. Hope to attend myself! #ShefChem16 | |
| WendyAnneW #ShefChem16 next up Ben Allen & Sree Vadlamudi on network pharmacology and NCEs | |
| baoilleach #ShefChem16 Ben Allen & Sree Vadlamudi e-Therapeutics; Network Pharmacology and New Chemical Entities | |
| nathanbroo Up next: two-hander of Ben Allen & Sree Vadlamudi from e-Therapeutics on Network Pharmacology & New Chemical Entities #ShefChem16 | |
| baoilleach #ShefChem16 What is Network Pharmacology? And how can it be used for drug discover? | |
| baoilleach #ShefChem16 Think about diseases in terms of interacting proteins | |
| nathanbroo Network pharmacology to elicit protein network of pathways and dependencies. Biology is highly robust & adaptive. #ShefChem16 | |
| baoilleach #ShefChem16 Network is robust and adaptive, emergent function | |
| baoilleach #ShefChem16 Nodes+edges=network, network props, node props, community structure | |
| WendyAnneW #ShefChem16 networks nodes and edges explained | |
| dgelemi #ShefChem16 2 part talks from e-therapeutics. 1st on work from 2 yrs ago and 2nd on the results from it and results from cell assay ("real") | |
| nathanbroo Many ways to characterise networks: diameters, distances, etc. Cf. social networks and small world phenomena #ShefChem16 | |
| baoilleach #ShefChem16 Network diameter (max dist) - to what extent the network can route around a missing edge | |
| WendyAnneW #ShefChem16 network and node properties discussed | |
| baoilleach #ShefChem16 Centrality of nodes - probably important - betweenness centrality - how often a node is on the shortest paths | |
| baoilleach #ShefChem16 Modules, cliques, clustering - parts of the network more connected to each other than to the rest - communities | |
| nathanbroo Community structure: identify functional organisation of complex networks. Biology: connection between structure & function. #ShefChem16 | |
| baoilleach #ShefChem16 Network science considers a random network; randomly deleting nodes makes them vulnerable | |
| baoilleach #ShefChem16 Biological networks have power-law degree distribution (not gaussian); robust to random node deletion but vulnerable to targeted | |
| WendyAnneW #ShefChem16 vulnerability of networks. Biol networks have no inherent scale and are robust | |
| nathanbroo Biological networks have power-law degree distribution. Robust to random node deletion; brittle to targeted node deletion. #ShefChem16 | |
| baoilleach #ShefChem16 Apply to perturb a protein-protein interaction network - intervention needs to be both multiple and targeted | |
| dgelemi #ShefChem16 lots of stats on biological networks. They are not like random and it's possible to identify important nodes | |
| baoilleach #ShefChem16 Can disrupt by picking highest degree and highest betweenness nodes | |
| WendyAnneW #ShefChem16 he explains how to disrupt a network | |
| baoilleach #ShefChem16 Have team of biologists spending between 2-6 mnths building up a network for a particular problem | |
| WendyAnneW #ShefChem16 you need experts to construct the networks from lit searching and pathway analysis | |
| baoilleach #ShefChem16 Cmpds are promiscuous binders and pleiotropic (?) in action | |
| baoilleach #ShefChem16 Overall footprint of cmpd affects many targets and if hit the right ones, then can have important network effect | |
| dgelemi #ShefChem16 expert biologists involve for months to help building networks with some meaning #manualwork | |
| baoilleach #ShefChem16 Have an in-house target profile database used to identify molecules that will have the desired effect | |
| nathanbroo Generate large profile of compound-protein interactions, filling in the gaps using chemoinformatics. Not elaborated. #ShefChem16 | |
| WendyAnneW #ShefChem16 looking for compounds that have a significant effect on the network but don't care about structures of ligand or protein | |
| baoilleach #ShefChem16 Very spare dataset as full interaction profile not known from ChEMBL | |
| WendyAnneW #ShefChem16 yes pleiotropic | |
| nathanbroo As-is tends to mine known pharmacology. Now moving into predictive models to find the unknown. #ShefChem16 | |
| dgelemi #ShefChem16 Bayesian and fingerprint mentioned as prediction. Try to predict the entire pharmacology profile of a compound | |
| WendyAnneW #ShefChem16 network impact calculation explained | |
| baoilleach #ShefChem16 Network impact calculation: cmpd->footprint in database->remove corresponding nodes from network (approximation) | |
| baoilleach #ShefChem16 Then calculate impact on network, e.g. largest component size and so forth...now for some of the results | |
| nathanbroo Moving on to Sree to talk about case studies using network pharmacologies. #ShefChem16 | |
| WendyAnneW #ShefChem16 @baoilleach sparse not spare | |
| dgelemi #ShefChem16 end of part 1. Now real application as a result of investment in 2012 to use the platform that was developed before that. | |
| nathanbroo Heavy use of CROs for compound acquisition and testing. #ShefChem16 | |
| baoilleach #ShefChem16 12 projects currently happening; some in med chem/lead opt stage; immuno-oncology, oncology, CNS | |
| nathanbroo 12 projects; 30-35 FTEs working over Europe. Two cancer projects presented: telomerase and hedgehog. #ShefChem16 | |
| WendyAnneW #ShefChem16 oncology projects are of the most interest to them | |
| baoilleach #ShefChem16 Telomerase: involved in cell cycle reg, exp of anti-apoptotic proteins; DNA damage response, reg of Myc, NF-kB and Wnt/b-catenin | |
| baoilleach #ShefChem16 Hypothesis: telomerase is involved in many cancer-related activities | |
| dgelemi #ShefChem16 e-therapeutics moving as a biotech with assay dvt, hit and lead discovery with CROs. Focus on cancer, CNS and inflammation | |
| WendyAnneW #ShefChem16 new telomerase biology - approach for targeting the telomerase | |
| baoilleach @WendyAnneWarr #ShefChem16 Spot the journal editor :-) | |
| baoilleach #ShefChem16 Telomerase project: had cmpds that hit 4 or 5 key nodes, bought similar cmpds for screening | |
| nathanbroo Identify compounds that impact 4 or 5 nodes. #ShefChem16 | |
| baoilleach #ShefChem16 Test cmpds in cell proliferation assays, and telomerase expression in two cancer cell lines | |
| dgelemi #ShefChem16 find compounds that affect key nodes from network with telomerase. And test in cell assays to show reduction of expression | |
| baoilleach #ShefChem16 393 cmpds tested; 106 active, 9 selected based on potentcy/selectivity/IP, lead opt -> 100x improvement, patents filed | |
| baoilleach #ShefChem16 Targeting the hedgehog pathway (?) | |
| WendyAnneW #ShefChem16 non SMO based methods for hedgehog binding | |
| baoilleach #ShefChem16 | |
| nathanbroo Rapid progress of initial hits through optimisation of drug parameters: potency, DMPK, etc. #ShefChem16 | |
| dgelemi #ShefChem16 aim for candidate nomination be W4 2016 after lead optimisation effort to improve activity by 100 fold. #success | |
| baoilleach @WendyAnneWarr #ShefChem16 "when" not "where" | |
| dgelemi #ShefChem16 first 3D pie chart of the conference #dataviz . Good result to show out of it though | |
| baoilleach #ShefChem16 (...oops lost the thread, responding to typo corrections) | |
| DrJoshuaBo Difference between network and graph? Networks are graphs where nodes/edges have attributes... Sounds like a graph to me! #ShefChem16 | |
| baoilleach #ShefChem16 This time, 1146 cmpds tested, 63 active, lead opt --> 1000x potency improvement, multiple patents filed, scale up planned | |
| nathanbroo Both cancer targets expected to deliver in candidate selection in Q4 2016. #ShefChem16 | |
| WendyAnneW #ShefChem16 they also have TNF projects but no time to discuss | |
| baoilleach #ShefChem16 Summary: unique discov platform; proven ability to id high-potency mols; open to collab | |
| deniseOme .@nathanbroon @baoilleach + extraction of target-disease associations @EuropePMC http://phenoday2016.bio-lark.org/pdf/1.pdf #ShefChem16 pic.twitter.com/lwiwmeMd7w | |
| WendyAnneW #ShefChem16 they have unique patent protected platform... | |
| dgelemi #ShefChem16 @baoilleach no mention on the medchem side and how hit to lead is done (usual way assumed). e-Platform for target/hit ID | |
| baoilleach #ShefChem16 Planning on developing MOA platform to generate more info for networks | |
| baoilleach #ShefChem16 Question: isn't optimising for 1 target hard enough, without thinking about 5 simultaneously? | |
| baoilleach #ShefChem16 Answer: not straightforward, is different than one-target opt | |
| WendyAnneW #ShefChem16 next up Tim James of Evotec. Building a phenotypic screening collection | |
| nathanbroo Challenge of optimising selectively to dive targets is huge but confident can progress. Multiple chemotypes so backups available #ShefChem16 | |
| dgelemi #ShefChem16 @baoilleach from the answer I got they do phenotypic screening and not target based. Am I correct? @nathanbroon @WendyAnneWarr | |
| baoilleach #ShefChem16 Tim James Evotec: challenges in optimising public data for phenotypic screening | |
| nathanbroo Up next: Tim James on building & analysing phenotypic screening collections from Evotec #ShefChem16 | |
| baoilleach #ShefChem16 Phenotypic drug discov - work backwards from readouts to identify targets | |
| dgelemi #ShefChem16 next one is #evotec for phenotypic screening by Tim James and analysing data results and some target assignment | |
| baoilleach #ShefChem16 HTS library - potential to find novel targets no-one has worked on before - exptal techniques work but not always | |
| WendyAnneW #ShefChem16 HTS library has potential for finding novel targets but bios note the | |
| nathanbroo CROs have access to large amounts of data but they don't own it so can't use it. #ShefChem16 | |
| WendyAnneW #ShefChem16...bio annotate the library. Sorry for previous typo | |
| baoilleach #ShefChem16 Using bioannotated library - target not novel, but the connection to a particular disease is | |
| baoilleach #ShefChem16 Screening data + annotations -> initial hypotheses | |
| WendyAnneW #ShefChem16 thus establish some initial hypotheses. Then assess the target using much publicly available data | |
| baoilleach #ShefChem16 Target assessment: knockout/mutation data, clinical data (SIDER/ClinVar), pharmac. probes, literature evidence | |
| baoilleach #ShefChem16 Target validation: experimental, CRISPR, siRNA, overexpression | |
| WendyAnneW #ShefChem16 experts then do target validation. He will home in on target de convolution | |
| baoilleach #ShefChem16 Approaches to target deconvolution: cheminf is part of a set of complementary techniques, e.g. phospho-proteomics | |
| nathanbroo Many approaches to target deconvolution. Chemoinformatics not used in "glorious isolation" #ShefChem16 | |
| nathanbroo ~5k unique drugs, probes, tools in phenotypic screening library. #ShefChem16 | |
| baoilleach #ShefChem16 Bioannotated screening collection: maximise target coverage while mininising cmpd duplication, 8019 samples->5k unique | |
| nathanbroo Key point on (any) screening library: continuous curation! #ShefChem16 | |
| baoilleach #ShefChem16 Manual checking of chemical structures; 800 fixes for stereo; outright errors; subtle errors | |
| WendyAnneW #ShefChem16 much curation of the bio annotated library must be done manually eg stereo | |
| dgelemi #ShefChem16 use bio annotated library as screening deck for target deconvolution in phenotypic screening. Similar to Irwin lab at UCSF | |
| baoilleach #ShefChem16 Important to have correct structures or everything that follows will be wrong if the structure is wrong | |
| nathanbroo Database is bio annotated from multiple sources: @ChEMBL, DrugBank, @pubchem (qualitative), suppliers & manual curation. #ShefChem16 | |
| baoilleach #ShefChem16 ~900k data pts from public sources (e.g. PubChem, DrugBank, ChEMBL), 172 data pts per cmpd | |
| WendyAnneW #ShefChem16 manual curation for quantitative activities | |
| baoilleach #ShefChem16 Text-mining the supplier descriptions identifies 2549 compound-target annotations | |
| baoilleach #ShefChem16 (aside: am wondering whether patents could also be a useful source of cmpd target annotations) | |
| WendyAnneW #ShefChem16 text mining for compound-target annotations covers only 35% of the collection | |
| nathanbroo Using public data 60-70% of data can be corroborated. #ShefChem16 | |
| baoilleach #ShefChem16 Comparing annotations from multiple sources: 60% consistent, 6% inconsistent | |
| WendyAnneW #ShefChem16 most of the cmpds are relatively selective | |
| georgeisyo @nathanbroon best quote so far #shefchem16 | |
| baoilleach #ShefChem16 Assessing polypharmacology: most cmpds appear selective, median of 0 for highly-potent, median of 3 for <= 10 micromolar | |
| WendyAnneW #ShefChem16 19% of cmpds have no annotation at all for activity | |
| nathanbroo Highly polypharmacological compounds should be retired: promiscuous, false-positives (assay tech.)? #ShefChem16 | |
| baoilleach #ShefChem16 Quality of biological probes: PAINS 7%, MedChem flagged 12%, Badapple 22% flagged | |
| baoilleach #ShefChem16 Quinalizarin both a catechol and a quinone, appears to be potent at 16 different targets | |
| WendyAnneW #ShefChem16 up to 20% of cmpds have problems re PAINS and the like | |
| baoilleach #ShefChem16 Why does 19% of the collection lack any protein-level annotations: prodrugs, antibiotics/virals/non-human, poorly characterised | |
| baoilleach #ShefChem16 Need to retire some cmpds and find more data for others | |
| baoilleach #ShefChem16 Do statistical analysis to find enriched targets, enriched GO terms, enriched pathways | |
| WendyAnneW #ShefChem16 from phenotypic screen to MOA | |
| baoilleach #ShefChem16 Even for the top targets, we find both cmpds known to be active, and active - we want to see the weight of evidence | |
| baoilleach #ShefChem16 (Missed how the stats are worked out) | |
| baoilleach #ShefChem16 What other data sources: STITCH, .... | |
| WendyAnneW #ShefChem16 how to extend the coverage of the library. Integrity and Reaxys are expensive | |
| baoilleach #ShefChem16 Assessing library coverage; we cover ~80% of target space, but requiring selectivity reduces this down to 20%, but dn't needthis | |
| WendyAnneW #ShefChem16 ChEMBL and she lectivity.. | |
| WendyAnneW #ShefChem16 oops - selectivity | |
| dgelemi #ShefChem16 difficulty to get a library of properly annotated compounds, manual effort, expensive other source of information... | |
| baoilleach #ShefChem16 Context is important - cmpd would interact but not permeable into cell - could ChEMBL tell this? not much data available on this | |
| WendyAnneW #ShefChem16 establishing context is critical and it cannot be done from public data | |
| dgelemi #ShefChem16 using @ChEMBL for permeability data is not fantastic. Few data points published. | |
| nathanbroo Summary: knowledge-driven phenotypic deconvolution can be done using public data (to a certain extent). Curation essential! #ShefChem16 | |
| baoilleach #ShefChem16 Summary: pharmacology data well reported but contextual data not; data curation important; public data CAN be used for this | |
| nathanbroo I'm chairing the next session so tweets probably at a minimum. @WendyAnneWarr, @baoilleach & @dgelemi will be tweeting harder! #ShefChem16 | |
| dgelemi #ShefChem16 @baoilleach getting ready for next session.pic.twitter.com/z1Q9KK9pLL | |
| baoilleach #ShefChem16 Next up is my boss, Roger Sayle, on Chemical Similarity based on Graph Edit Distance | |
| WendyAnneW #ShefChem16 after the coffee break we have Roger Sayle on chem similarity based on graph edit distance | |
| dgelemi #ShefChem16 @nathanbroon preparing to chair next session. Starting with @nmsoftwarepic.twitter.com/vOXONiXfcg | |
| PedroFranc #ShefChem16 pic.twitter.com/ntBWtyLzv1 | |
| baoilleach #ShefChem16 Tribute to Andy Grant, who was involved back at the start in 2012 | |
| baoilleach #ShefChem16 2D chemical similarity - fingerprints or MCS-based approaches (shoutout to Ed Duesbury) | |
| baoilleach #ShefChem16 MCS results more intuitive but more difficult to implement - shoutout to Val Gillet | |
| baoilleach #ShefChem16 Limitations of fps: sim based on local substructure "all the same notes just in a different order" | |
| baoilleach #ShefChem16 Can have saturation of features/chemical space (e.g. Andrew Dalke's Chemical Tookit Rosetta benzodiazapine set) | |
| baoilleach #ShefChem16 fps: no distinct between atom type changes (e.g. Cl->Br), tautomers may have low similarity | |
| dgelemi #ShefChem16 fingerprints have limitations. Local substructures, difficulty for peptides, C16+ alkanes, atom type and stereochemistry | |
| baoilleach #ShefChem16 Examples: Central change in structure leads to low Tanimoto | |
| baoilleach #ShefChem16 Given the known deficiencies with binary fps, is it possible to do better with modern hardware? | |
| dgelemi #ShefChem16 Genesis of small world from @nmsoftware https://www.nextmovesoftware.com/smallworld.html | |
| WendyAnneW #ShefChem16 can we do better with 2D similarity. JCIM 46, 1912 | |
| baoilleach #ShefChem16 Grant et al JCIM 2006 - Lingos - "the emperor has no clothes" - a simple method that beats path-based fps | |
| baoilleach #ShefChem16 41% of mols in dataset with same Bemis-Murcko scaffold have same bioactivity; for MMPs, it's 86%, same mol formula, it's 83% | |
| baoilleach #ShefChem16 R-groups (anti-scaffold); 50% of the time same bioactivity (keep the R groups and throw away the scaffolds) | |
| baoilleach #ShefChem16 String edit distance, but applied to molecular graphs | |
| WendyAnneW #ShefChem16 MMPs and mol formula are better than bemis Murcko for similarity of bio activity | |
| baoilleach #ShefChem16 1965 Levenshtein no. of insertions, deletions and subs to transform one string into another - big in bioinformatics | |
| baoilleach #ShefChem16 Graph Edit Distance is the same concept applied to graphs - Sanfeliu 1983 | |
| WendyAnneW #ShefChem16 advances in bioinformatics mean that GED matching is feasible | |
| baoilleach #ShefChem16 Examples of edits: benzene->pyridine 1 mutation; bezene->cyclohexane change of hybridisation; R group additions | |
| baoilleach #ShefChem16 Unfortunately, calculating GED is a generalisation of calculation of MCS which is NP-Hard | |
| WendyAnneW #ShefChem16 calcn of GED is generalisation of MCS. Computationally expensive | |
| baoilleach #ShefChem16 GED and MCS are related by deleting atoms from A to get the MCS, and then adding atoms to get B | |
| baoilleach #ShefChem16 Video of SmallWorld in action https://www.youtube.com/watch?v=hZ4QyQSeSWg | |
| nmsoftware #ShefChem16 Video of SmallWorld in action https://www.youtube.com/watch?v=hZ4QyQSeSWg | |
| baoilleach #ShefChem16 Shows demo - can fine-tune edit distance searching - turn on/off mutations | |
| dgelemi #ShefChem16 recorded video instead of live demo. Not taking risk with network | |
| WendyAnneW #ShefChem16 demo shows that it IS fast enough - so now onto the math | |
| baoilleach #ShefChem16 Possible to solve MCS problem in polynomial time using preprocessed graphs Messmer ref | |
| WendyAnneW #ShefChem16 the secret is in the preprocessing. Loads of sub graphs found and stored | |
| baoilleach #ShefChem16 Enumerate all subgraphs for a given molecule, sorted by the number of bonds. MCS becomes a trivial set intersection or lookup. | |
| dgelemi #ShefChem16 thanks to math research and pre processing it is now possible to do fast search. #crossdiscipline | |
| baoilleach #ShefChem16 A single molecule could have millions of subgraphs. Shows graph of molecule connected to its subgraphs, all the way down... | |
| baoilleach #ShefChem16 Can find all the molecules one atom away, two atoms away, etc. very quickly - find nearest neighbours even in large databases | |
| WendyAnneW #ShefChem16 his tree-like view of chemical space. In SmallWorld. One bond at base, structure with seven bonds higher up the tree | |
| baoilleach #ShefChem16 The bigger the database (e.g. PubChem) the faster the search for nearest neighbours - counter intuitive | |
| baoilleach #ShefChem16 Traditional MCS starts from one bonds and grow; SmallWOrld starts with N bonds and shrinks | |
| miltonbrew #ShefChem16 Build reaction fps based on difference between fps for product and reactant combinations | |
| baoilleach #ShefChem16 Grand unified theory of cheminf forces: mol identity, sub search, chem sim. All can be done with the SmallWorld graph database. | |
| WendyAnneW #ShefChem16 fraction of practical chemical space. Is actually much smaller than theoretical space | |
| baoilleach #ShefChem16 Different edge types in graph: terminal edges, link edges, ring gen/breaking | |
| baoilleach #ShefChem16 "Dalke wormholes" - need to work down from A to MCS and then go up, not vice versa to avoid wormholes through unlikely structure | |
| baoilleach #ShefChem16 Efficient subgraph enumeration - benzene has 7 anon subgraphs, atorvastatin has 3million | |
| WendyAnneW #ShefChem16 efficiencies in MCES. Eg delete just one bond | |
| baoilleach #ShefChem16 Roger's starting to go super fast! Slides going by in a whizz | |
| baoilleach #ShefChem16 Today db has 19billion nodes and 76billion edges, 2.85 terabytes | |
| WendyAnneW #ShefChem16 19 billion nodes. 76 billion edges In SmallWorld | |
| baoilleach #ShefChem16 Connected vs disconnected MCS | |
| WendyAnneW #ShefChem16 in SmallWorld index, that is | |
| WendyAnneW #ShefChem16 representing activity cliffs becomes intuitive | |
| baoilleach #ShefChem16 Representing activity cliffs - intuitive in terms of graph edit distance. Any molecule more similar to A than to B... | |
| baoilleach #ShefChem16 Summary: sublinear behaviour in searching, blessing of dimensionality, continual advances in hardware make this more attractive | |
| baoilleach #ShefChem16 Question: have considered applying weights to particular edit distances? | |
| baoilleach #ShefChem16 Answer: can be done, e.g. PAM matrices in biology, difficult to come up with consistent weights | |
| WendyAnneW #ShefChem16 in memorium Andrew Grant | |
| baoilleach #ShefChem16 Question: possible to work out GED between 2 molecules directly? Answer: yes - but not practical for db search | |
| WendyAnneW #ShefChem16 next up Richard Hall of Astex on their fragment network | |
| baoilleach #ShefChem16 Richard Hall Astex: The Astex Fragment Network | |
| dgelemi #ShefChem16 need more after talk. Slide on FP issues with protein and peptide but no example during the talk #shortoftime | |
| baoilleach #ShefChem16 More on graph networks and recommendation systems - to find near nbrs for hit validation | |
| WendyAnneW #ShefChem16 the Astex fragment network builds heavily on sayle's work | |
| baoilleach #ShefChem16 Networks used to manage Big Data - think of PageRank - we have built a graph network for chemistry recommendations | |
| dgelemi #ShefChem16 astex talk on their approach to similarity graph search. Comparison to smallworld from @nmsoftware is in the abstract | |
| baoilleach #ShefChem16 "Fragments like this may also be of interest to you" | |
| WendyAnneW #ShefChem16 Astex has graph network for chemistry recommendations | |
| baoilleach #ShefChem16 Fragment hit validation - do I really need the phenyl, can I grow the fragment in direction X, can I substitute on the ring? | |
| baoilleach #ShefChem16 Many of these are not matched pairs. | |
| DrJoshuaBo Roger Sayle presenting SmallWorld long in the making. Another tribute to the genius of Andy Grant #ShefChem16 | |
| baoilleach #ShefChem16 Path-based FPs don't work well in the fragment space. Substructure searching difficult to find diff rings, longer linkers, O/M/P | |
| baoilleach #ShefChem16 (ortho/meta/para substituted rings) | |
| dgelemi #ShefChem16 traditional fingerprint similarity measures don't work well on fragments. Similar fragments end up in the noise | |
| WendyAnneW #ShefChem16 chemical FPs do not work well for fragments. Substructure search may be laborious .network approach looks appealing | |
| baoilleach #ShefChem16 Generate a list of nodes and edges by systematically removing (not atoms) but rings, linkers and functional groups | |
| baoilleach #ShefChem16 Shows a graph where fragments are connected to subfragments | |
| DrJoshuaBo Remember Andy asking me to write depiction webservice for this. I added loads of features and of course it was too slow! #ShefChem16 | |
| WendyAnneW #ShefChem16 build network by removing rings linkers funct groups systematically | |
| WendyAnneW As network grows, we begin to connect similar cmpds #ShefChem16 | |
| baoilleach #ShefChem16 Network built up to include 4.5 million cmpds - nodes are labelled with source/supplier info | |
| WendyAnneW #ShefChem16 search network by looking for friends of friends | |
| baoilleach #ShefChem16 To search, we can easily find nbrs which are additions/deletions to a substrucutre, find nbrs of nbrs, search time 0.1-0.2s | |
| baoilleach #ShefChem16 Up to 16 heavy atoms (fragments) - Use neo4j 23M nodes, 107M edges - cypher query language | |
| WendyAnneW #ShefChem16 system called neo4j. | |
| baoilleach #ShefChem16 Grouping of results into sets, e.g. para-sub of upper ring, upper ring replacement, hydroxy group replacement | |
| nathanbroo Roger from @nmsoftware showing enumeration of alkanes that is similar to Arthur Cayley's kenograms. #ShefChem16 pic.twitter.com/jxPogMOsj5 | |
| baoilleach #ShefChem16 We sort the results by no of registry/ChEMBL observations - number of times particular subs occur - more likely subs shown first | |
| baoilleach #ShefChem16 Peter Ertl JS viewer used again (also by Roger Sayle) | |
| baoilleach #ShefChem16 Web-based simple interface - each result annotated with source information | |
| baoilleach #ShefChem16 Chemists love it - can find 95% of the interesting cmpds with 5% of the effort | |
| baoilleach #ShefChem16 Comparison to other sim measures (shoutout to #rdkit) | |
| baoilleach #ShefChem16 Gotchas: some changes are not as small as you might think, e.g. changing two OH to two OMe's | |
| WendyAnneW #ShefChem16 interesting comparison. Some frag net cmpds would be hidden in noise. A few outliers found by both methods. Many don't overlap | |
| baoilleach #ShefChem16 The search results are only for 2 edge distance (i.e. nbrs of nbrs) | |
| baoilleach #ShefChem16 Can't jump directly from ring to ring | |
| WendyAnneW #ShefChem16 chemists and modellers at Astex love it | |
| baoilleach #ShefChem16 Retrieves cmpds inline with med chem intuition | |
| baoilleach #ShefChem16 Future work: add additional edge types (e.g. tautomer, similar rings), add "like" functionality, more comparisons to fps | |
| baoilleach #ShefChem16 Analyse connectivity - might be a good source of variation (synthetically mutable) | |
| baoilleach #ShefChem16 (previous tweet is a reference to highly connected molecules in the database) | |
| baoilleach #ShefChem16 Landrum points out an atom-atom map sim method more appropriate for fragments | |
| baoilleach #ShefChem16 Andrew Dalke on Calvin Mooers and the early history of chemical information | |
| baoilleach #ShefChem16 Starting looking into history of how to substructure screens | |
| dgelemi #ShefChem16 next talk from Andrew Dalke on Calvin Mooers and history of chemical information #historyLecture | |
| baoilleach #ShefChem16 1940s "there was too much data" was the complaint - people wanted to do complex queries | |
| baoilleach #ShefChem16 People used punched cards (this is before computers were really around) | |
| dgelemi #ShefChem16 pictures of punched cards. Back to a time that I don't know #TooYoung | |
| baoilleach #ShefChem16 Two types: edge-notched for manual systems and interior-notched for sorting machines - use a knitting needle to search notches | |
| WendyAnneW #ShefChem16 I remember both types of punch cards but I never used edge-notched ones | |
| baoilleach #ShefChem16 Lots of punch card coding systems were developed - not a binary search system, was more complex than that | |
| baoilleach #ShefChem16 C&EN 1945: Frear stat investigation of correlation between structure and toxicity - Frear Codes - substructure keys | |
| baoilleach #ShefChem16 1945 "As we may think" - Vannevar Bush proposed a memex machine to search information - big inspiration at the time | |
| dgelemi #ShefChem16 big data in 1945 with 1M records in CAS. Start thinking on how to retrieve data and index it | |
| baoilleach #ShefChem16 Over 1 million records at CAS at the time - Chemistry was Big Data | |
| WendyAnneW #ShefChem16 first time I have heard of frear code but everyone knows about memex. Only 1 million records in CAS then | |
| baoilleach #ShefChem16 Calvin Mooers invented information retrieval at ACS conference 1950 (or st like this) | |
| baoilleach #ShefChem16 Moers coined the term descriptor. Is there a clever coding system for sparse data. | |
| nathanbroo Mooers coined the term "descriptors" #ShefChem16 | |
| WendyAnneW #ShefChem16 mooers coined the term descriptor | |
| baoilleach #ShefChem16 Used to call false negatives "false drops" due to physical dropping of cards from the needles | |
| baoilleach #ShefChem16 More and more complicated codes to support card searching of more than 1 descriptor and supporting "AND" queries | |
| WendyAnneW #ShefChem16 mooers and superimposition of random codes | |
| WendyAnneW #ShefChem16 Calvin Mooers that is... | |
| baoilleach #ShefChem16 Zatocoding - Sharpe and Dohme were the first customers | |
| dgelemi #ShefChem16 description on problem with punched card, needles and collisions. Created way to encode descriptors then moves to chemical | |
| nathanbroo Chemical Zatocoding by Mooers is the first time descriptors were used. #ShefChem16 | |
| baoilleach #ShefChem16 Indexing limitations - human error in indexing - a non-index search was very slow, e.g. 3 months search of CAS | |
| nathanbroo Information Retrieval has its roots in chemistry! #ShefChem16 | |
| WendyAnneW #ShefChem16 I just learned something from Noel. I never knew why we used the term "false drop" | |
| baoilleach #ShefChem16 Zatopleg coding - awesome name | |
| WendyAnneW #ShefChem16 I am beginning to feel young! I never heard of zatopleg coding | |
| baoilleach #ShefChem16 Substructure search desired as a constraint satisfaction problem suitable for early computers (UNIVAC) | |
| baoilleach #ShefChem16 Mooers strong armed people over the patent and was challenged with an injunction - took years of lawsuits to resolve | |
| dgelemi #ShefChem16 zatopleg coding. Not in Wikipedia! Best to go to Andrew Dalke website http://www.dalkescientific.com/writings/diary/archive/2014/06/19/Calvin_Mooers.html | |
| baoilleach #ShefChem16 Mooers started working at National Bureau of Standards. Came up with N-tuples (early Daylight fps?) of atoms. | |
| baoilleach #ShefChem16 1960s: Can finally buy a computer! | |
| WendyAnneW #ShefChem16 but surely we have all heard of Ray and kirsch and their connection table. They referenced mooers | |
| baoilleach #ShefChem16 Ernst Meyer at BASF implemented Mooers contraint satisfaction sub search | |
| baoilleach #ShefChem16 CIDS project by the US army | |
| WendyAnneW #ShefChem16 Meyer at BASF computerised mooers work in GREMAS in the 60s | |
| WendyAnneW #ShefChem16 mooers is referenced in the Morgan paper too | |
| baoilleach #ShefChem16 Canonicalisation. Gluck published an algorithm. Mooers is mentioned by Morgan (1965), and may have suggested the algorithm. | |
| baoilleach #ShefChem16 Dysonian ciphers, ordering zermelo - more awesome names | |
| dgelemi #ShefChem16 chemical space can be defined by graphs, in 1960's | |
| WendyAnneW #ShefChem16 by 1973-1975 references to mooers disappear | |
| WendyAnneW #ShefChem16 lefkovitz cited mooers | |
| baoilleach #ShefChem16 Mooers' influence lasted till 70s - e.g. Lefkovitz (1975) "the large db file structure dilemma" | |
| dgelemi @WendyAnneWarr #ShefChem16 but Sheffield was on the slide for the 70's. Difficult to tweet and follow this talk. Lots of unknown stuff | |
| baoilleach #ShefChem16 Feldman and Hodes (1975) - a superimposed code for substructure searching | |
| WendyAnneW #ShefChem16 so did Feldman and Hodes. I visited Feldman once, many years ago | |
| dgelemi #ShefChem16 and here is the daylight slide https://twitter.com/baoilleach/status/750286354697838592 | |
| baoilleach #ShefChem16 summary of zatocoding problems, and now the impact on cheminf | |
| WendyAnneW #ShefChem16 zatocoding does help explain daylight fp work | |
| baoilleach #ShefChem16 Mooers - "Father of the conection table"? Yes, though reinvented multiple times | |
| baoilleach #ShefChem16 The biologists liked the encoding better than the chemists | |
| baoilleach #ShefChem16 Thanks to the Charles Babbage Institute - Minnesota | |
| dgelemi #ShefChem16 makes you wonder what those scientifically in the 40/60's would have done with modern computers! #foundation | |
| dgelemi #ShefChem16 lunch breaks down poster session. May need it as next talk as integral equation theory in the title | |
| rguha @dgelemi thankful for the lunch break. So I catch up on the deluge that is #ShefChem16 | |
| dgelemi #ShefChem16 MMP and pharmacophores. But different definition of pharmacophores from @Discngine graphs MMPspic.twitter.com/KaNeLrc50t | |
| baoilleach #ShefChem16 David Palmer on "Is there a role for the integral eqn theory of mol liquids in chem informatics?" | |
| baoilleach #ShefChem16 Go Dave! | |
| baoilleach #ShefChem16 LogP/LogD etc all rely on solvent effects - can we calc these a bit more rigorously and quickly? | |
| dgelemi @rguha and we're back with RISM-MOL-INF (obvious) #ShefChem16 | |
| baoilleach #ShefChem16 Hydration free E - how much the mol wants to be in gas or soln phase | |
| WendyAnneW #ShefChem16 David Palmer univ Strathclyde integral equation theory of molecular liquids is a statistical mechanics method. Not my field | |
| baoilleach #ShefChem16 pKa, solub, lipophilicity, complex formation all linked to hydration free E | |
| baoilleach #ShefChem16 Two main methods: explicit solvent (MD, MC - rigourous but slow) versus implicit (solvent distrib fns - continiuum electrostats) | |
| baoilleach #ShefChem16 Another implicit method - integral eqn theory of liquids - fairly quick - more info than continuum models | |
| baoilleach #ShefChem16 "Fairly shocking slide to see shortly after lunch" - the equation for the 3D RISM method | |
| WendyAnneW Yes all those 3D RISM equations are a bit of a blow after lunch #ShefChem16 | |
| baoilleach @pwk2013 #ShefChem16 Yes - through the MCS. | |
| baoilleach #ShefChem16 Around since the 1930s, but not used much because doesn't give great results. | |
| baoilleach #ShefChem16 But Palmer et al 2010 have shown that including the Partial molecular volume that make it work | |
| dr_greg_la @WendyAnneWarr yeah, it's not that much math, but still brutal after the food. #ShefChem16 | |
| baoilleach #ShefChem16 Extrapolates from dataset of 185 molecules to drug-like molecules - very robust | |
| WendyAnneW Jctc 2014, 10, 934 #ShefChem16 | |
| baoilleach #ShefChem16 Original model had 2 regression params, cavity correction since then (1 param), ensemble correction (0 param) | |
| WendyAnneW J phys chem let 2014 5 1935 #ShefChem16 | |
| dgelemi #ShefChem16 improvement from the 1st equation by different groups and a new one in press. Longer and longer equations to correct for errors | |
| baoilleach #ShefChem16 He has developed a PC+ Correction (JChemPhys2015 in press) using the knowledge of the others | |
| WendyAnneW #ShefChem16 his own work is j phys chem in press | |
| baoilleach #ShefChem16 No empirical params so doesn't just work for neutral molecules in standard conditions | |
| WendyAnneW J chem phys 2015, 142, 091105 #ShefChem16 | |
| baoilleach #ShefChem16 Works for ionized small organics - the MD calcs take 1000 times longer | |
| WendyAnneW #ShefChem16 j phys chem b 2016, 120, 975 | |
| rguha fears of overfitting? #ShefChem16 https://twitter.com/dgelemi/status/750316190946525184 | |
| baoilleach #ShefChem16 Recently been working on salting in/out - can predict Setschenow's constants very well | |
| baoilleach #ShefChem16 Non-polar solvents can be modelled as Lennard-Jones spheres.. More difficult to solve the equations due to more atoms. | |
| baoilleach #ShefChem16 Using a coarse-grained model to handle this. Well-studied problem in chemical engineering so using info from that. | |
| baoilleach #ShefChem16 Doesn't work so well for non-spherical solutes, e.g. long linear alkanes | |
| WendyAnneW J phys chem b in press on nonpolar solvents #ShefChem16 | |
| baoilleach #ShefChem16 Prediction of solubility from mol simulation - try to do it from first principles - still a difficult problem | |
| baoilleach #ShefChem16 Not yet accurate enough for practical applications. Need to move to higher levels of theory. | |
| baoilleach #ShefChem16 Want a fast general method to predict a number of different outputs - should be fast enough for screening databases | |
| baoilleach #ShefChem16 If we can compute the solvent density very quickly how can this be converted to useful descriptors? | |
| baoilleach #ShefChem16 Showing the RISM functional from 0 -> 20 Ang. Integral is the hydration free energy. Shape is specific to the specific molecule. | |
| baoilleach #ShefChem16 Put them into machine-learning algorithms, e.g. random forest. Mol Pharm 2015 Very accurate predictions | |
| baoilleach #ShefChem16 One conformer used, but what about averaging over many? Shoutout for confab #openbabel | |
| baoilleach #ShefChem16 Included SAMPL data + Mobley et al - the multiconf method works slightly better (or is it worse?) | |
| baoilleach #ShefChem16 Caco-2 permeability - Mol Pharm 2015 - we do okay - as good or better than others | |
| baoilleach #ShefChem16 Using 1D RISM - could move to 3D RISM - should improve things but slower | |
| baoilleach #ShefChem16 Review article in Chem Rev just submitted | |
| baoilleach #ShefChem16 Working on intrinsic aqueous solubility - need to get that right first. There's a systematic error - from the xtal term. | |
| WendyAnneW #ShefChem16 David Palmer won a CSA Trust grant, I remember | |
| baoilleach #ShefChem16 ....but very little data available on sublimation thermodynamics. | |
| baoilleach #ShefChem16 The 1st solvation shell tends to be the best predictor in the models. | |
| baoilleach #ShefChem16 Sereina Riniker Teaching distance geometry about exptal torsion prefs | |
| baoilleach @pwk2013 #ShefChem16 Sorry - am dropping the ball on answering - will have to catch up later.... | |
| WendyAnneW #ShefChem16 Sereina Riniker on conformed generator | |
| dgelemi #ShefChem16 next is Sereina Riniker on her work on 3D conformation with @dr_greg_landrum . Method already in @RDKit_org | |
| baoilleach #ShefChem16 Conformer generation - many applications - want diverse structures - how to validate? | |
| baoilleach #ShefChem16 Methods: Systematic search (ltd to few rotatable bonds) vs stochastic search (can handle highly flexible mols but may miss stuf) | |
| baoilleach #ShefChem16 Knowledge based methods, distance-geometry based | |
| baoilleach #ShefChem16 Distance geom - have a distance bounds matrix based on connectivity and try to embed into 3D Blaney/Dixon/RevCompChem1994 | |
| baoilleach #ShefChem16 How it's done. 1-2 distances, 1-3 distances, 1-4, other distances. #rdkit algorithm explained step by step. | |
| baoilleach #ShefChem16 v. fast - only needs 2D structure. Disadvantage - can lead to "not so good" looking conformations | |
| baoilleach #ShefChem16 Normally a forcefield opt is done. Our idea is to improve this by using exptal torsion prefs. | |
| WendyAnneW #ShefChem16 distance geometry DG fast but embedding can lead to some not-pretty conformers | |
| baoilleach #ShefChem16 Rarey published paper with 392 SMARTS with torsion prefs. | |
| baoilleach #ShefChem16 In the end, we regenerated everything from the CSD data. Still not enough. Added "basic knowledge" terms for minimization step. | |
| WendyAnneW #ShefChem16 JCIM 2015, 55, 2562 Riniker and landrum | |
| baoilleach #ShefChem16 ETKDG: exptal torsions + knowledge distance geometry | |
| WendyAnneW #ShefChem16 it is called ETKDG | |
| baoilleach #ShefChem16 Showing examples of fitted torsion potentials, and comparing to Rarey paper | |
| baoilleach #ShefChem16 Datasets: CSD dataset (Hawkins 2010) and PDB dataset, and some more from CSD | |
| baoilleach #ShefChem16 Looked at RMSD to xtal structure for best conformer (symmetry corrected) | |
| baoilleach #ShefChem16 Riniker and Landrum J Cheminf 2015 | |
| WendyAnneW ETKDG outperforms DG both by rmsd and tfd analysis #ShefChem16 | |
| baoilleach #ShefChem16 Within 1Ang can reproduce 80% of the CSD molecules - comparison to Confect | |
| WendyAnneW #ShefChem16 also compared it with CONFECT | |
| WendyAnneW #ShefChem16 the analysis is in the Riniker landrum paper in JCIM 2015 | |
| baoilleach #ShefChem16 Examples where basic DG beats ETKDG and v.v. | |
| baoilleach #ShefChem16 ETKDG is 2-3 times slower than basic DG, but double the speed of DG+UFF opt, | |
| WendyAnneW #ShefChem16 of course it takes 2 or 3 times as long to carry out compared with DG | |
| baoilleach #ShefChem16 Further devel: chirality issues in DG fixed, improved planarity for aromatic rings and sp2-atoms | |
| WendyAnneW #ShefChem16 since publication chirality issues fixed | |
| baoilleach #ShefChem16 ETKDG v 2 : updated torsion SMARTS pattern Guba et al JCIM 2016, now 408 patterns, 364 in original | |
| WendyAnneW #ShefChem16 JCIM 2016 56 1 | |
| baoilleach #ShefChem16 ETKDG v 3 : additional torsion patterns for aliphatic ring bonds, currently DG generated | |
| WendyAnneW #ShefChem16 next version will include aliphatic rings torsion patterns | |
| baoilleach #ShefChem16 (aside: nice to see academic developing software that becomes part of a toolkit...just like Confab!) | |
| baoilleach #ShefChem16 Summary: DG+exptal torsion prefs is a good way to create conformers | |
| baoilleach #ShefChem16 Torsion deviation fingerprint also used to assess performance | |
| baoilleach #ShefChem16 Rarey makes the point that handling intermolecular hydrogen bonds might actually be a bad idea for biological uses | |
| baoilleach #ShefChem16 David Schaller Target-pairs for synergistic bio activity | |
| dr_greg_la @WendyAnneWarr But! Similar accuracy with 1/4 the number of conformers #ShefChem16 | |
| WendyAnneW #ShefChem16 David schaller trying too identify drug-target pairs | |
| baoilleach #ShefChem16 For each target we set a different threshold automatically based on the data | |
| WendyAnneW #ShefChem16 try again... Trying to identify drug-target pairs by systematic data mining | |
| baoilleach #ShefChem16 Need to separate out data on agonist, antagonist | |
| dgelemi #ShefChem16 next talk is David Schaller on finding multi targets that can be used for a disease. @ChEMBL mentioned in first few slides | |
| baoilleach #ShefChem16 Working on obesity - causies diabetes, cardio diseases and cancer | |
| baoilleach #ShefChem16 A set of different targets described: PPARa, LEPR, GHSR, 41 targets in total with act in ChEMBL | |
| baoilleach #ShefChem16 In total had 57k activities - first challenge is to filter with a cutoff | |
| dgelemi #ShefChem16 workflow in @knime around filtering chembl. Need to consider different activity threshold per target | |
| WendyAnneW #ShefChem16 41 targets with activity data in chem bl. First challenge is activity filtering. Problem if you set a threshold. | |
| baoilleach #ShefChem16 Set the threshold 3 log units higher than the most active and use that as a cutoff | |
| WendyAnneW #ShefChem16 diff thresh | |
| WendyAnneW #ShefChem16 diff threshold needed for each target... | |
| baoilleach #ShefChem16 Sometimes this is too strict, or too high. So needed to set max and min values. | |
| WendyAnneW #ShefChem16 so activity threshold borders done for 41 targets | |
| baoilleach #ShefChem16 Less well discovered targets tended to have higher thresholds, of course. | |
| WendyAnneW #ShefChem16 ziprasidone target families discu send | |
| baoilleach #ShefChem16 Target families; merged all subtypes into one, e.g. all 5-HT, dopamine receptors. | |
| WendyAnneW #ShefChem16 and now to chemical similarity | |
| WendyAnneW #ShefChem16 21 targets 14 families and ? | |
| baoilleach #ShefChem16 Down to 235 compounds now that show activities over different target families. | |
| dgelemi #ShefChem16 keeping only if similarity between compounds is high, FCFP>0.7. Only 235 activities left | |
| baoilleach #ShefChem16 Data divided into agonist/partial agonist vs antagonist/inv agonist/inhib - must be done manually - not in ChEMBL typically | |
| WendyAnneW #ShefChem16 getting the activity type E.g. agonist is important and that is not in chem bl. So do manually | |
| baoilleach #ShefChem16 Want to find synergistic effects, two targets with one compound | |
| rguha a somewhat different usage of ‘synergy’. Is this actually looking for dual inhibitors? #ShefChem16 https://twitter.com/baoilleach/status/750330245912363009 | |
| WendyAnneW #ShefChem16 h3r and mchr1 receptors | |
| baoilleach #ShefChem16 Need to hit two targets to avoid cancelling out inhibitory effect of receptor | |
| baoilleach #ShefChem16 Manual validation - predictions - 3 hits < 1 micromolar | |
| WendyAnneW #ShefChem16 known h3r antag plus some bought in similar cmpds were shape matched - work I n progress | |
| baoilleach #ShefChem16 ..brain starting to drift from moorings...my fault, not speaker | |
| WendyAnneW #ShefChem16 11 target pairs found inc one confirmed | |
| baoilleach #ShefChem16 All done with Knime | |
| baoilleach #ShefChem16 Summary: found potential target-pairs for appetite inhibition, and have found some potential hits | |
| WendyAnneW #ShefChem16 I omitted some of the similarity matrices and manual validation. Hard to listen well and type well at the same time | |
| georgeisyo Data mining for dual pharmacology using @ChEMBL data and @knime. Great logo too. #ShefChem16 pic.twitter.com/VU3kihCCBp | |
| WendyAnneW #ShefChem16 wonder if there will be more of those yummy scones with jam and cream for tea. | |
| jezwicker Mmm, donuts, best coffee break ever #ShefChem16 pic.twitter.com/FbMco6wmD8 | |
| baoilleach @dgelemi #ShefChem16 @rguha It was that slide with the picture of the axons. | |
| WendyAnneW #ShefChem16 I preferred the scones to the doughnuts | |
| baoilleach #ShefChem16 Sebastian Salentin PLIP Fully auto protein-ligand interaction profiler | |
| WendyAnneW #ShefChem16 https://GitHub | |
| baoilleach #ShefChem16 A tool to detect relevant non-covalent interactions from PDB structures | |
| baoilleach #ShefChem16 Published in NAR web server issue | |
| WendyAnneW #ShefChem16 oops! http://GitHub.com/ssalentin/plip | |
| baoilleach #ShefChem16 Overview: lists an overview | |
| baoilleach #ShefChem16 ligand-binding depends on specific patterns of non-covalent interactions | |
| baoilleach #ShefChem16 Diff types: polar interactions (H bonds, salt bridges, water bridges, halogen bonds), aromatic interactions (pi cation+stacking) | |
| baoilleach #ShefChem16 People saw we don't need to consider halogen bonding and other less frequent ones but.... | |
| dgelemi #ShefChem16 multiple types of interactions and even if not some are not frequent, they can be important. | |
| baoilleach #ShefChem16 ...these are sometimes v important for particular targets, e.g. ABC-transporter (pi catio interactions in several directions) | |
| baoilleach #ShefChem16 Why useful to know? For viz, characterisation (e.g. design, docking/md - can postprocess), comparison (repositioning drugs) | |
| WendyAnneW #ShefChem16 can be used in visualisation characterisation and comparison of interactions | |
| baoilleach #ShefChem16 We are interested in drug repositioning - using microarray data, polypharmacology networks | |
| baoilleach #ShefChem16 Looking at pairs of targets with similar bindings sites - look for similar interaction patterns for drug repositioning | |
| WendyAnneW #ShefChem16 he uses plip in drug repositioning | |
| baoilleach #ShefChem16 Web server and open source command line tol: fast, few seconds per structure, easy-to-use one-click, 8 interaction types | |
| baoilleach #ShefChem16 ...soon, will have REST interface and protein-protein interactions | |
| baoilleach #ShefChem16 Shows example image created automatically, which is publication-ready | |
| WendyAnneW #ShefChem16 fast. No need to prepare pdb file. Easy to use. Publication ready images. 8 interaction types | |
| WendyAnneW #ShefChem16 soon they will offer REST service and will handle protein protein interactions | |
| baoilleach #ShefChem16 Example shown of DNA/RNA interaction diagram. Takes care of the assembly of the whole strand without any input from the user. | |
| baoilleach #ShefChem16 Can provide PDB code, 3D viz in JSMOL, can download PyMol session file, all results also available as XML/RST (human readable) | |
| dgelemi #ShefChem16 interface a bit simpler than CREDO (http://marid.bioc.cam.ac.uk/credo ), and user can upload their own file | |
| baoilleach #ShefChem16 Distance and angular constraints applied for different interactions to help identify interactions | |
| baoilleach #ShefChem16 Can automate over large numbers of proteins and tune params, e.g. for low resolution | |
| baoilleach #ShefChem16 Application: docking postprocessing | |
| baoilleach #ShefChem16 Interaction profiles help identify false positives in docking - let's say you want halogen bonds in your ligand | |
| WendyAnneW #ShefChem16 application. Helps identify false positives in docking. (Been published) | |
| WendyAnneW #ShefChem16 another app is binding mode matrices - work in progress | |
| baoilleach #ShefChem16 Working on tools to handle an ensemble of complexes - i.e. different ligands to same targets - and visualise diffs in bndngmodes | |
| baoilleach #ShefChem16 Repositioning: search for similar interaction features + binding sites | |
| dgelemi #ShefChem16 using interactions to predict new targets with similarity by interaction features | |
| WendyAnneW #ShefChem16 target prediction PLoS one 2013 discovering similarity | |
| ZINClick #ShefChem16 Just used PLIP on my phone, very easy and fast! (pdb id: 2gvj) @ssalentinpic.twitter.com/iYw9A8P9bW | |
| baoilleach #ShefChem16 Virtual screening of 125K PDB complexes based on interaction profile (I think) - followed by docking of 200 candidates | |
| WendyAnneW #ShefChem16 hsp27 pattern matching pipeline shown. 2 out of 6 hits validated | |
| baoilleach #ShefChem16 ...docking into Hsp27 and TK - exptal validation - 2/6 validated hits in functional assay and cells | |
| baoilleach #ShefChem16 Interaction fingerprinting: ligand-centric + protein-centric -> fusion concept (multiple proteins - multiple ligands) | |
| WendyAnneW #ShefChem16 interaction fingerprinting is future work | |
| baoilleach #ShefChem16 Plip web service URL too long to type - Google it | |
| baoilleach #ShefChem16 Available on PyPi and Github Apache v2.0 | |
| baoilleach #ShefChem16 PLIP uses #openbabel (yay!) | |
| WendyAnneW #ShefChem16 I have tweeted the URL | |
| baoilleach #ShefChem16 Sergio Ruiz Dynamic undocking and the quasi-bound state as tools for drug design | |
| baoilleach #ShefChem16 Barcelona group working on MD, chemoinformatics, and virtual screening, target validation, new chemical probes, non-standrd MMoA | |
| WendyAnneW #ShefChem16 ruiz-carmona on dynamic undocking DUck and quasi-bound state WQB | |
| baoilleach #ShefChem16 rDock is the docking program developed in the group | |
| WendyAnneW #ShefChem16 rDock published in PLoS comput. Biol | |
| baoilleach #ShefChem16 Speed vs accuracy tradeoff in molecular docking | |
| baoilleach #ShefChem16 Goal to develop novel tools complementary to existing methods to improve their performance | |
| baoilleach #ShefChem16 Quasi-bound state - what is it? Delta G binding - diff between bound+unbound. | |
| WendyAnneW #ShefChem16 manuscript has been submitted on DUck and WQB | |
| baoilleach #ShefChem16 Forget about energy of binding. Instead, diff ligands have diff E profiles. | |
| dgelemi #ShefChem16 presentation work from Ruiz-Carmona has been submitted and under review. | |
| baoilleach #ShefChem16 Hypothesis: active ligands have narrow delta G minimum, i.e. structurally stable. Test whether resistant to small perturbation. | |
| baoilleach #ShefChem16 Quasi-bound: point along the dissociation cure where it has just broken the most important contacts | |
| WendyAnneW #ShefChem16 h bond interaction potentials have deep and narrow minima | |
| baoilleach #ShefChem16 H-bond interaction potentials are deep and narrow. Water-shielded H bonds present steep barriers (strongly resist breaking) | |
| baoilleach #ShefChem16 Most proteins contain an essential H bond, fulfilled by all ligands, even the smallest ligands form at least one H-bond | |
| WendyAnneW #ShefChem16 water shielded h bonds present steep barriers | |
| baoilleach #ShefChem16 We think that stability comes from H bonds and we use them to calculate stability. | |
| baoilleach #ShefChem16 Quasi bound: ...where the ligand has just broken the key essential (predefined) H bond | |
| WendyAnneW #ShefChem16 most Prots contain an h bond. And one more reason... | |
| baoilleach #ShefChem16 Dynamic undocking - DUck - particular implementation of steered MD - measure work to reach quasi-bound state | |
| WendyAnneW #ShefChem16 DUck is implementation Of steered molecular dynamics SMD | |
| WendyAnneW #ShefChem16 work to reach quasi-bound state = WQB | |
| baoilleach #ShefChem16 When doing the MD, only keep a protein portion, local environment, explicit water solvation, equilibrate (1ns): 20min (GPU) | |
| baoilleach #ShefChem16 SMD from 2.5A -> 5.0A in 0.5ns: 10min, work is the maximum along the curve | |
| WendyAnneW #ShefChem16 DUck can discriminate between active and inactive cdk2 ligands | |
| baoilleach #ShefChem16 Hmmm...not sure how they are placing the actives/inactives in the binding site? | |
| baoilleach #ShefChem16 Shows a distribution of the work value is shifted to smaller values for the inactives vs actives | |
| dgelemi @baoilleach #ShefChem16 yes it does. nice ROC scores with examples presented (0.85) | |
| WendyAnneW #ShefChem16 AUC 0.85 for cdk2 or 0.87 for aa2r or 0.86 for another | |
| WendyAnneW #ShefChem16 docking and DUck are complementary | |
| baoilleach #ShefChem16 Docking and DUck are complementary - docking has *All* interactions, DUck focuses on a single interaction | |
| baoilleach #ShefChem16 "Dock...then DUck!" | |
| WendyAnneW #ShefChem16 dock then DUck ! | |
| baoilleach #ShefChem16 Comparing histograms of actives/inactives along both axes of a scatter plot of docking scores and work scores. | |
| baoilleach #ShefChem16 (aside: this work reminds me of my work on receptor depth scaling in GOLD - i.e. upscore deep H bonds in docking) | |
| WendyAnneW #ShefChem16 work with vernalis on prospective app to hsp90 virtual screen | |
| baoilleach #ShefChem16 Prospective application to Hsp90 (seems to be a popular target at this meeting) | |
| baoilleach #ShefChem16 8M->500 topscoring->139 candidates->28 strong Work -> tested 21 -> 8 active (38%) vs 4.4% from fragment screening | |
| baoilleach #ShefChem16 Here's that URL for PLIP... https://twitter.com/ssalentin/status/750355387711365120 | |
| dgelemi #ShefChem16 on prospective work the undocking method is good at removing false positive from docking. | |
| WendyAnneW #ShefChem16 strong binders -bought & tested 21 compounds. 8 active in fragment screens. Medium 15 bought 1 active. Weak of 11 none active | |
| baoilleach #ShefChem16 Collab with Vernalis | |
| WendyAnneW #ShefChem16 sorry Only 4.4% by fragment screening but 38% of strong etc. NOT 8 tested in fragment screens | |
| WendyAnneW #ShefChem16 Agnes Meyder is last speaker of the day. From the Rarey group | |
| baoilleach #ShefChem16 Agnes Meyder EDIA: Estimating electron density support for individual atoms in X-ray structures | |
| baoilleach #ShefChem16 Works on rescoring Hyde results - wanted to improve quality of x ray structures in benchmarks | |
| baoilleach #ShefChem16 Want to highlight well-supported versus unsupported atoms to visually analyse ligand, but also automatically score it | |
| WendyAnneW #ShefChem16 rescoring with Hyde . Support prot structure with electron density. Identify hi quality data for benchmarks | |
| baoilleach #ShefChem16 Various pose comparison methods: RMSE, IBAC, GARD, RSE, but what about exptal uncertainties? | |
| baoilleach #ShefChem16 Comparing model and expt: RSR, RSRn, RSCC, RSZD - compare observed and calcd electron density | |
| WendyAnneW #ShefChem16 shortcomings of current comparisons outlined. RSR, RSZD etc. | |
| baoilleach #ShefChem16 RSR and friends bad (intuitive but several probs); RSZD good (statistically sound, reproducible, needs diff map) | |
| WendyAnneW #ShefChem16 EDIA = estimating electron density support for individual atoms in X-Ray structures | |
| baoilleach #ShefChem16 New scoring method! Compute score on 2f0-fc map; atomwise; score should be comparable between structs iwth diff resolutions | |
| baoilleach #ShefChem16 Published in Nittinger et al 2015 | |
| baoilleach #ShefChem16 EDIA reports unexpectedly shaped electron density, overlapping electron d spheres between atoms, ranges from 0 -> 1.2 | |
| WendyAnneW #ShefChem16 EDIA initially reported in JCIM 2015, 15, page? | |
| baoilleach #ShefChem16 Method: oversample electron d grid; assign electron d radius to each atom; more details...read the paper everyone | |
| baoilleach #ShefChem16 Method now described in pictures, starting to sound like stock/stakeholder partitioning used for partial charges | |
| baoilleach #ShefChem16 (or is it Mulliken charges I'm thinking of) | |
| baoilleach #ShefChem16 Outer vs inner electron density sphere - I've missed out what this is I'm afraid | |
| baoilleach #ShefChem16 Can identify unaccounted density, not enough density, and possible clashes | |
| baoilleach #ShefChem16 Given the atomic EDIAs, we can work out the EDIA for a set of atoms - a sort of weighted sum that falls off quickly if unspprted | |
| baoilleach #ShefChem16 Nice diagrams displaying different possibilities for EDIA for single atom - even I can understand this one | |
| baoilleach #ShefChem16 EDIA stability analysis - EDIA>0.8 good, 0.4 medium and less than that it's bad (for resolution of up to 2 Ang) | |
| baoilleach #ShefChem16 Poorer resolution is not suitable for EDIA analysis - electron d probably not Gaussian which is one of the assumptions | |
| baoilleach #ShefChem16 Looked at correlation of EDIA with RSCC - dataset is combined Iridium HT - Astex set | |
| baoilleach #ShefChem16 RSCC only checks for shape of electron d; EDIA checks for shape and density | |
| baoilleach #ShefChem16 Applications: identify multiple confs, bogus single atoms (e.g. chlorine) | |
| WendyAnneW #ShefChem16 apps. Can detect clashes | |
| WendyAnneW #ShefChem16 example of too much density | |
| baoilleach #ShefChem16 Showing some nice examples of PDB errors | |
| WendyAnneW #ShefChem16 alternative conformation problem detection | |
| WendyAnneW #ShefChem16 water versus chlorine issue | |
| baoilleach #ShefChem16 Can screen the whole of the PDB, subset thereof with 2.0A rez | |
| WendyAnneW #ShefChem16 now some comments on metals in pdb | |
| WendyAnneW #ShefChem16 could also use EDIA to curate hi quality benchmark datasets | |
| baoilleach #ShefChem16 Features: intuitive scoring scheme, integratd fault analysis, 10s per binding pocket, good for curation, analysis | |
| WendyAnneW #ShefChem16 clearly an important paper. lots of questions even tho we are running late | |
| WendyAnneW #ShefChem16 conference dinner in Sheffield cathedral tonight | |
| nathanbroo Conference dinner in Sheffield Cathedral. ##ShefChem16 pic.twitter.com/PfQRxyE3Hr | |
| WillPitt1 Professor Peter Willet and @WendyAnneWarr at #ShefChem16 dinner pic.twitter.com/mgEvpLl3lP | |
| nathanbroo We're playing guess the molecule with Andrew Dalke's homemade index punch cards. #ShefChem16 pic.twitter.com/WSSmusLND7 | |
| nathanbroo Guess who is having this scurvy fighting cocktail? #ShefChem16 pic.twitter.com/GkM5xGl1Z6 | |
| nathanbroo Index cards for chemical structure lookup by Andrew Dalke. I am in awe!!! #ShefChem16 pic.twitter.com/eHA1GsHH2d | |
| dgelemi #ShefChem16 and we're back for last days. 1st speaker is H. J. Patel from Oxford. Lots of logos, diamond, ucb, GSK, sgc, epsrc, mrc... | |
| baoilleach #ShefChem16 Hannah Jemi Patel Novelty Score: Prioritising cmpds that are novel scaffolds or form novel PL interactions | |
| baoilleach #ShefChem16 First talk ever at a conference - good luck! | |
| baoilleach #ShefChem16 Drug discov process: subjective decision making - synthetic intuition, existing biases, experience | |
| mvkrier #shefchem16 Starting today with @HannahJemi from @UniofOxford: which compound to make next? The Novelty Score | |
| baoilleach #ShefChem16 Novelty score: primary goal of drug discov is identification of novel active cmpds | |
| baoilleach #ShefChem16 Prioritise molecules that are most diff to known binders in terms of interactions with protein, placement in binding site, struc | |
| baoilleach #ShefChem16 Algorithm: create three grids after aligning protein structures, 3D matched molecular pairs mentioned,.. | |
| nathanbroo Last conference breakfast. #ShefChem16 pic.twitter.com/tfPQiinyiM | |
| baoilleach #ShefChem16 Protonate protein with PDB2PQR, calculate pharmacophoric features with #rdkit | |
| baoilleach #ShefChem16 Create grid that contains counts of elements, also a grid of counts of pharmacophoric features, include xtal occupancy too | |
| baoilleach #ShefChem16 Interaction defns taken from PLIP (see talk yesterday) | |
| dgelemi #ShefChem16 PLIP for protein interaction presented yesterday is mentioned as the source of features | |
| baoilleach #ShefChem16 Place candidate cmpds into binding site with 3D MMPs - after alignment to library, constrained conf generation with rdkit | |
| nathanbroo Up now: Hannah Patel from Oxford Uni talking about prioritising molecules for protein-Logan interactions. #ShefChem16 | |
| baoilleach #ShefChem16 Score based on grid - something like how novel the structure is compared to the grids based on elements, pharm feats, interacts | |
| baoilleach #ShefChem16 For interactions, also takes into account the direction vector rather than just counts | |
| nathanbroo Now for some scaffold hopping: prioritise novelty in elements and making same interactions. #ShefChem16 | |
| baoilleach #ShefChem16 Interpretation: can prioritise scaffold hopping - want novel element score, but deprior novel interaction score | |
| baoilleach #ShefChem16 ...or can prioritise novel interactions | |
| nathanbroo Novelty: prioritise pharmacophore and interaction score. #ShefChem16 | |
| WendyAnneW #ShefChem16 Patel high element score low interaction score for scaffold hopping | |
| mvkrier #shefchem16 CRANks is web-browser based | |
| baoilleach #ShefChem16 Showing web server demo; spreadsheet of results, showing mols on left, scores and distributions to their right | |
| WendyAnneW #ShefChem16 they are developing a way of visualising the grid | |
| nathanbroo Live demo! Clear and interactive interface. #ShefChem16 | |
| jezwicker Great demo of Novelty Score by Hannah Patel from University of Oxford #ShefChem16 | |
| baoilleach #ShefChem16 Showing 3D structure view of aligned proteins and showing the grids. Can load a conformer in and see how overlaps. | |
| dgelemi #ShefChem16 live demo of the web service currently in dvt. It's called CRANKs, interactions visible by lines or grid points with cpd or not | |
| baoilleach #ShefChem16 Only in first year of PhD! A lot done... | |
| WendyAnneW #ShefChem16 very preliminary testing with hiv 1 protease | |
| baoilleach #ShefChem16 Preliminary test: example HIV-1 protease, lots of data available, high mutation rate so need to be careful to match seq identity | |
| baoilleach #ShefChem16 Shows two hydrophobic regions with split in-between where the catalytic site is | |
| nathanbroo #ShefChem16 comparison with other dissimilarity methods. | |
| baoilleach #ShefChem16 Comparison to using fps for same purpose; novelty score can separate molecules used or not to create the grid but fps cannot | |
| WendyAnneW #ShefChem16 she is testing grid on brd1 | |
| nathanbroo #ShefChem16 planning to test on BRD1 in future: testing ranking of novelty score for enrichment & relationship of novelty score & others. | |
| baoilleach #ShefChem16 Future: testing on more proteins, can rank actives vs inactives? more comparisons to other methods, param tuning, handle water? | |
| WendyAnneW #ShefChem16 in future will dev algorithm with pharmacophore features and waters | |
| dgelemi #ShefChem16 future dvt will be to include the water. Hot topic at the moment | |
| nathanbroo #ShefChem16 planning to include waters in future, particularly bridging waters. | |
| baoilleach #ShefChem16 Collab with GSK, UCB, Diamond | |
| dgelemi #ShefChem16 presentation with HIV protease leads to questions about it, old target > expert in the room (mutation, flexibility) | |
| baoilleach #ShefChem16 Oliver Koch - Analysing the framework of PL interactions | |
| baoilleach #ShefChem16 Overall topic is binding site comparison | |
| mvkrier #shefchem16 Oliver Koch from @TU_Dortmund will talk about ligand sensing cores and privileged scores. | |
| baoilleach #ShefChem16 Movie time - GSK3 & TryS have similar ligand sensing cores - pass the popcorn... | |
| mvkrier #shefchem16 overall binding site comparison; example GSK3 & TryS | |
| WendyAnneW #ShefChem16 Oliver Koch from Dortmund movie of gsk3 and TryS. Similar ligand sensing cores | |
| baoilleach #ShefChem16 Secondary structural element arrangement are similar though overall binding modes are different | |
| nathanbroo Up now: Oliver Koch from Dortmund. Identifying privileged scaffolds. Lots of scaffolds this morning. #ShefChem16 | |
| mvkrier #shefchem16 have a look at #pdb 1q3w and 2vpm | |
| WendyAnneW #ShefChem16 in trypanosomatid diseases. Koch has chapter in vch 2013 book | |
| baoilleach #ShefChem16 Both targets have ligands that have similar scaffold, but different side chains | |
| WendyAnneW #ShefChem16 future med chem 2011 3 699 secondary protein structure | |
| baoilleach #ShefChem16 Does nature reuse specific folding pattern to recreate particular binding site. Skolnick 2015 | |
| baoilleach #ShefChem16 Similar backbone - can lead similar but also dissimilar pockets | |
| nathanbroo #ShefChem16 different view on molecular design today. This time metaphor is shape sorting toy. | |
| WendyAnneW #ShefChem16 Skolnik bioorg med chem lett 2015 15:25(6) 1163 pocket space and shape | |
| nathanbroo #ShefChem16 ligand sensing cores and molecular scaffolds: lots of possibilities of binding different proteins. | |
| baoilleach #ShefChem16 Emphasising the interplay of both mol scaffold and protein structural elements | |
| HannahJemi First talk at a conference complete! *phew* Thanks for having me! #ShefChem16 #relieved | |
| mvkrier #shefchem16 nature likes symmetry; pattern recognition | |
| dr_greg_la So great to have a mix of talks from people early in their careers and more experienced researchers. #ShefChem16 | |
| WendyAnneW #ShefChem16 it is not just like shape sorting there are "wheels within wh eels". Now use in Identification of new ligands | |
| baoilleach #ShefChem16 Needed software to compare only secondary structural elements around binding site - no such software available- did it ourselves | |
| baoilleach #ShefChem16 Scaffoldhunter to generate scaffolds from bioactivity data | |
| baoilleach #ShefChem16 Combined with protein target info to find scaffolds annotated with target info | |
| WendyAnneW #ShefChem16 similar scaffold, different targets. Found a novel ligand sensing core | |
| baoilleach #ShefChem16 Applying to find new ligands: wanted new inhibs for BRD4 | |
| dgelemi #ShefChem16 goal of O. Koch is to find privileged scaffolds where the selectivity is found by decorations, using a rationale approach. | |
| mvkrier to get here http://scaffoldhunter.sourceforge.net/ #scaffolds #ShefChem16 https://twitter.com/baoilleach/status/750608804514062337 | |
| nathanbroo #ShefChem16 using ScaffoldHunter tool to identify new & interesting scaffolds: http://scaffoldhunter.sourceforge.net pic.twitter.com/QFhfAjRy2Y | |
| dgelemi #ShefChem16 on privileged scaffold, a review here http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2908274/ , and examples in tables 1 and 4 | |
| baoilleach #ShefChem16 4 proteins with ligand info; sim search of screening library and tested; some hits (0, 1, 3, 11% hit rate) | |
| WendyAnneW #ShefChem16 brd4 similar to 4 other proteins. Get their 4 ligands. To 4 libraries. Test vs brd4. Hits confirmd for prot a and b, and best c | |
| WendyAnneW #ShefChem16 so new brd4 inhibitors found | |
| nathanbroo #ShefChem16 new BRD4 inhibitors identified & validated with thermal shift and crystallography. | |
| baoilleach #ShefChem16 Also did docking: hit rate 5%, but hits from ligand sensing core approach were almost completely different | |
| WendyAnneW Now Docking based vs tried as well. Only 2 cmpds similar from the two approaches #ShefChem16 | |
| baoilleach #ShefChem16 Runtimes are fast: compare 3 pockets to 3000 takes 11s, 39k x 39k is 1.5d | |
| baoilleach #ShefChem16 How to do pocket detection? ligand-based or automated? | |
| WendyAnneW #ShefChem16 automated pocket selection methods not robust | |
| dgelemi #ShefChem16 the hypothesis: similar scaffold of binding sites bind to same compound scaffold can be validated | |
| baoilleach #ShefChem16 Ligsite - automated pocket detection not so good for MAO example | |
| WendyAnneW #ShefChem16 the pocket detection method used can have major effect on your results | |
| nathanbroo #ShefChem16 automated pocket detection method: Ligsite. | |
| baoilleach #ShefChem16 ...but method so fast that no need to rely on automated pocket detection - just compare whole protein if necessary | |
| WendyAnneW #ShefChem16 he does not have to rely on auto pocket detection | |
| baoilleach #ShefChem16 Enzyme classification can be done based on ligand-sensing cores - shows network with different regions for different classes | |
| nathanbroo #ShefChem16 looks like @Gephi used for network visualisation but not specified. | |
| WendyAnneW #ShefChem16 all against all comparison of pdb. Had to use auto pocket detection in this case | |
| WendyAnneW #ShefChem16 comparison took about 1 month on up to date workstation | |
| baoilleach #ShefChem16 Can do all-against-all on PDB (1 month of time) - comparing scores to GRAlign and Usearch | |
| WendyAnneW #ShefChem16 interesting pocket pairs being analysed now | |
| baoilleach #ShefChem16 Showing interesting pairs of proteins found as having similar pockets from the PDB | |
| WendyAnneW #ShefChem16 TryS versus pdb atp subset work in progress | |
| baoilleach #ShefChem16 Developing ligands for kinase based on tubulin...synthetase (TryS) | |
| WendyAnneW #ShefChem16 use in finding selective ligands | |
| baoilleach #ShefChem16 LOOKING FOR A POST-DOC | |
| baoilleach #ShefChem16 Everyone invited to Fulda for GCC | |
| WendyAnneW #ShefChem16 he is looking for a postdoc | |
| deniseOme @WendyAnneWarr I wonder if these 4 proteins correspond to the ones displayed in @targetvalidate? #ShefChem16 pic.twitter.com/PPYS6gOZ75 | |
| baoilleach #ShefChem16 "Really embarrassing" exit of people who haven't checked out yet - tut tut | |
| WendyAnneW #ShefChem16 Lena Kalinowsky diverse test set for validating scoring function | |
| baoilleach #ShefChem16 Lena Kalinowsky: Diverse test set for validation of scoring fns based on MMPs | |
| WendyAnneW #ShefChem16 ...based on matched molecular pairs | |
| baoilleach #ShefChem16 Many studies evaluating scoring fns for pose pred, ranking, and affinity pred | |
| baoilleach #ShefChem16 Docking problem solved in the case of rigid receptors, scoring problem not yet solved though | |
| baoilleach #ShefChem16 Most scoring fns correlate more with mol size than with binding affinity - want to address this issue | |
| WendyAnneW #ShefChem16 JCIM 54, 1717. Wang in JMC. Kitchen in nature... All have compared scoring functions. Scores match mol size better than affinit | |
| baoilleach #ShefChem16 Novel validation dataset based on MMPs | |
| baoilleach #ShefChem16 MMPs differ by one well defined transformation; e.g. H -> CH3, with corresponding effect on Ki | |
| baoilleach #ShefChem16 (Nice idea) | |
| baoilleach #ShefChem16 Starting pt was PDBbind - broad collection of data for many targets | |
| WendyAnneW Dataset of MMPs made from PDB bind #ShefChem16 | |
| baoilleach #ShefChem16 We used the core set of PDBBind - 195 diverse structures and binding data | |
| baoilleach #ShefChem16 Assigned PDB IDs to UniProt codes (oh oh I don't know bioinformatics..) | |
| WendyAnneW Assign to uniprot iD and cluster #ShefChem16 | |
| baoilleach #ShefChem16 Clustered based on sequence alignment (I think...) | |
| WendyAnneW #ShefChem16 sequence ID better than 90% used | |
| baoilleach #ShefChem16 Overall 2137 protein-ligand complexes were present | |
| baoilleach #ShefChem16 MMPs generated by Knime Erl Wood nodes | |
| baoilleach #ShefChem16 Sequence alignment within one MMP (seq id >= 90%)...more details..leading to 10200 3D MMPs | |
| WendyAnneW #ShefChem16 thus 2127 prot ligand pairs. MMPs generated. Seq. align. Verify orientation and loc of ligands | |
| WendyAnneW #ShefChem16 restrictions on MMPs explained | |
| baoilleach #ShefChem16 Some restricitions: cyclic substituents were restricted to max 9 nonH atoms, others to 5 nonH atoms, sub should be max 50%of mol | |
| baoilleach #ShefChem16 IC50 only accepted if from same publication --> now only 1224 MMPs diverse in terms of transformation | |
| baoilleach #ShefChem16 Now want diversity in terms of the targets and the bindings affinity effect | |
| baoilleach #ShefChem16 This left 99 3D MMPs, arranged into 33 target clusters | |
| nathanbroo #ShefChem16 interesting talk on 3D Matched Molecular Pairs leading to 99 in test set for validating scoring functions. | |
| WendyAnneW #ShefChem16 now 1224 pairs left. Now apply diversity in terms of target and binding affinity. Now only 99 MMPs in 33 targt clusters left | |
| baoilleach #ShefChem16 Can the scoring fns reproduce the affinity differences between the MMPs? | |
| WendyAnneW #ShefChem16 receptor preparation without water | |
| baoilleach #ShefChem16 Scoring fns: FF-based (GBVI/WSA, GoldScore, AutoDock 4.2) | |
| baoilleach #ShefChem16 Knowledge-based scoring fns: ASP, AutoDock Vina, DSX | |
| baoilleach #ShefChem16 Empirical: MOE London dG, X-Score, ChemPLP, ChemScore | |
| WendyAnneW #ShefChem16 there are force field, knowledge and empirical based scoring functions | |
| nathanbroo #ShefChem16 investigating also the effects of modelling with and without waters to see what effects they may have on scoring. | |
| baoilleach #ShefChem16 No docking - re-scoring was used | |
| baoilleach #ShefChem16 Transformation effect: Score A - Score B = result | |
| nathanbroo #ShefChem16 no docking conducted, only rescoring. Wonder if this has an effect on the score if it's not used in pose generation. | |
| WendyAnneW #ShefChem16 distributions in the dates | |
| baoilleach #ShefChem16 Seems to have matched series of length 3 for each target if I understood correctly | |
| baoilleach #ShefChem16 Showing pie charts to give idea of the diversity - maybe a histogram better? | |
| WendyAnneW #ShefChem16 distributions of affinity, transformation effect, and transformations in set shown. | |
| baoilleach #ShefChem16 Showing Pearson correlations - not sure of what exactly - missed that... | |
| baoilleach #ShefChem16 All correlations look very low, best is R of 0.36, rule of thumb is that R of 0.8 for pred of binding affinity | |
| WendyAnneW #ShefChem16 scoring functions and Pearson coefficient tabulated (with and without water). Best were X-score and consensus Not up to 80% tho | |
| dgelemi #ShefChem16 correlation between scoring function and delta activity. With Pearson correlation, no correlation, best R score is 0.36 | |
| baoilleach #ShefChem16 Do I gain or lose affinity, rather than predict exact value? How often the scoring fn predicted the correct trend... | |
| WendyAnneW #ShefChem16 trend of transformation effect on affinity. X-score and consensus good but only About 60% | |
| baoilleach #ShefChem16 Best is now 69% of the time. X-Score. ASP also good. | |
| dgelemi #ShefChem16 however trends (improve/decrease) are better for the scoring functions. (Pearson correlation not appropriate) | |
| baoilleach #ShefChem16 Overall 61% of the 3D-MMPs had increased affinity if increased size of the R group | |
| WendyAnneW #ShefChem16 correlation between mol size and score in 78% of cases | |
| WendyAnneW #ShefChem16 some transformations where function fails to predict Correct effect | |
| baoilleach #ShefChem16 Showing example of increased affinity from H->CH3; only corrected prediction if water molecules considered | |
| baoilleach #ShefChem16 A pyrazole->benzene example | |
| WendyAnneW #ShefChem16 only asp works well here | |
| WendyAnneW #ShefChem16 worse results usually if you consider waters | |
| nathanbroo #ShefChem16 plan is to release set of 3D-MMPs to the community for evaluation studies. | |
| baoilleach #ShefChem16 Dataset of diverse 99 MMPs - considering water mols leads in general to *worse* results - dataset will be made available | |
| DrJoshuaBo @HannahJemi Congrats, great talk. A bit of a baptism of fire! #ShefChem16 | |
| dgelemi #ShefChem16 current validation of the test set with external collaborator. Then set will be published, however can contact to access now | |
| nathanbroo #ShefChem16 looks like scoring functions are just no good at predicting activity. Waters make it worse. Massively significant, if true! | |
| baoilleach #ShefChem16 Advert for Big Data MGMS 1-day meeting at UCL early Sept @mgmsupdates | |
| baoilleach #ShefChem16 Bob Clark Estimating the uncertainty of ensemble regression model predictions | |
| WendyAnneW #ShefChem16 last session is the qsar one. First up Bob Clark estimating uncertainty of ensemble regression model predictions | |
| baoilleach #ShefChem16 "Lots of statistics but we can't escape" | |
| nathanbroo Up next: my old friend Bob Clark from @SimulationsPlus talking about estimating uncertainty in model predictions. #ShefChem16 | |
| baoilleach #ShefChem16 Med chemists should care about uncertainty because it's hard to balance lost opps versus risk of wasted effort | |
| nathanbroo Uncertainty important for chemistry, biology, management and regulators. #ShefChem16 | |
| mvkrier #shefchem16 Robert (Bob) D. Clark from @SimulationsPlus warns us that his talk will include a lot of #statistics | |
| baoilleach #ShefChem16 Biologists should care because in silico replications can help prioritise when they need to redo assays | |
| WendyAnneW #ShefChem16 medchemists biologists managers regulators should all care about uncertainty | |
| baoilleach #ShefChem16 Management should care because of costs (many types thereof) | |
| baoilleach #ShefChem16 Regulators need to know whether to trust QSAR/QSPR results | |
| baoilleach #ShefChem16 Pooling errors assumes that all preds are equally uncertain, but in fact ensemble analysis can pinpoint which are more trusty | |
| WendyAnneW Redistribute errors based on Gamma analysis of ENSEMBLE model #ShefChem16 | |
| baoilleach #ShefChem16 For this, we need to do dataset partitioning - not like train/test/verify | |
| baoilleach #ShefChem16 We need more data - because we need to sample and see the spread of data - cut out 10->20% of the data for building model, ... | |
| InfoSchool The final day of #ShefChem16 is in full swing! pic.twitter.com/MoKmbqM9gW | |
| baoilleach #ShefChem16 ..the rest for uncertainty estimation | |
| baoilleach #ShefChem16 Ensembles of neural networks - each network sees the same test set an val set but sees a diff scrambling of the training pool | |
| WendyAnneW #ShefChem16 80 to 90% of your data set must be in validation set. Neural networks in the ensemble | |
| baoilleach #ShefChem16 To estimate uncertainty, look at to what extent different networks in the ensemble agree | |
| WendyAnneW #ShefChem16 how much agreement among all the networks in the ensemble used to estimate uncertainty | |
| baoilleach #ShefChem16 Try to avoid the "shadow" of the training set on the test set | |
| baoilleach #ShefChem16 What would independ binomial errors look like? | |
| WendyAnneW #ShefChem16 looking for logp greater or less than 2 | |
| baoilleach #ShefChem16 Look at tally of positive votes (from multiple networks) and estimating particular error rates from sensitivity/specificity | |
| baoilleach #ShefChem16 GIven the no of positive votes, what is the chance that it is correct? | |
| WendyAnneW #ShefChem16 curves for tally of positive votes shown. The models are NOT independent | |
| baoilleach #ShefChem16 Actual curves look quite different than ideal curves, but not completely hopeless. The models are not completely independ. | |
| nathanbroo #ShefChem16 beta binomials: a set of conjugate binomials that mimics cases where trials are not independent. | |
| baoilleach #ShefChem16 Beta binomial distribution - a conjugate dist - a set of binomials. This mimics cases where trials are not indep. Better agrees. | |
| WendyAnneW #ShefChem16 beta binomial distributions are useful tools for uncertainty analysis | |
| baoilleach #ShefChem16 The better your model, the worse the sampling in the centre - where 50% of the networks disagree. | |
| WendyAnneW #ShefChem16 it really does work see j cheminf 2014 6 34 | |
| baoilleach #ShefChem16 Showing validation set results. Fits well the curve from the training data. Clark J Cheminf. 2014 | |
| baoilleach #ShefChem16 Given the no of positive votes for a particular obs, this is the confidence/uncertainty | |
| WendyAnneW #ShefChem16 people like to think of confidence rather than uncertainty | |
| baoilleach #ShefChem16 Kolmogorov-Smirnov statistic - hang on, I think I've used this one | |
| baoilleach #ShefChem16 ...used to assess fit via cumulative distributions | |
| WendyAnneW #ShefChem16 kolmogorov-Smirnov statistic... | |
| nathanbroo #ShefChem16 here is Bob's paper on beta binomials: https://jcheminf.springeropen.com/articles/10.1186/1758-2946-6-34 #OpenAccesspic.twitter.com/Fzn30Kir9q | |
| HannahJemi Dinner at Sheffield Cathedral last night #ShefChem16 @ Sheffield… https://www.instagram.com/p/BHhDbl1AEBNgtBE6XeSGZMhjg9MNoMIDuJEjAo0/ | |
| baoilleach #ShefChem16 Solubility prediction results shown | |
| WendyAnneW #ShefChem16 solubility regression model q-squared train set .86. And .81 Validation set | |
| baoilleach #ShefChem16 Errors should follow chi-squared if identically & independently distributed (IID) | |
| baoilleach #ShefChem16 qq plot - what value do you see vs what value you expect if normally distributed | |
| WendyAnneW #ShefChem16 normalised squared errors should follow Chi-squared | |
| baoilleach #ShefChem16 qq plot shows points where the stddev is underestimated versus overestimated | |
| baoilleach #ShefChem16 Conclusion: sigma and s? different for different cmpds - gamma distribs to the rescue... | |
| baoilleach #ShefChem16 ...once it is applied then the qq plot shows the points directly along the 45 degree line | |
| WendyAnneW #ShefChem16 gamma distributions to the rescue - math skimmed over. UnpUblished work | |
| baoilleach #ShefChem16 Explains how the gamma distribution is worked out - lots of details - manuscript in preparation so stay tuned | |
| baoilleach #ShefChem16 The validation set plot allows you to make inferences about which preds are good/bad/ugly; also help discard dubious values;... | |
| nathanbroo #ShefChem16 "If you lie to a model often enough, it will begin to agree with you." - Bob Clark | |
| baoilleach #ShefChem16 ...also, it highlights where all the networks get it wrong. "if you lie to the model enough times it will get it wrong" | |
| baoilleach #ShefChem16 Some binning in diagrams of results. Makes me feel uneasy. But now shows full dataset - just too many to show on one graph. | |
| WendyAnneW #ShefChem16 plot scaled squared errors . 84% of points are greater than or equal to 2. We are now getting the numbers right in logS predict | |
| baoilleach #ShefChem16 Looking at errors: detergents at top end, flame retardants at bottom end | |
| baoilleach #ShefChem16 Toluene sulfonic acid is super soluble - more like water dissolved in it, than v.v. | |
| WendyAnneW #ShefChem16 a few examples with excessive chi-squared. All pull water into a soggy crystal and are underestimated | |
| baoilleach #ShefChem16 Ryo Yoshida: A Bayesian algorithm for finding novel small organic molecules | |
| WendyAnneW #ShefChem16 Ryo Yoshdia Bayesian algorithm for finding novel small organic mols | |
| nathanbroo Up next: Ryo Yoshdia on Bayesian modelling for finding novel small molecules. Self-defined newcomer to the field. Welcome! #ShefChem16 | |
| baoilleach #ShefChem16 Goal to create novel promising hypothetical mols that exhibit desired props under multi-objective design | |
| dgelemi #ShefChem16 using the method in different fields in the recent years. From drug design to organic material and now inorganic | |
| nathanbroo #ShefChem16 transforming forward models into the backward prediction. | |
| WendyAnneW #ShefChem16 sorry - his name is Yoshida. It was wrong on his slide! | |
| dgelemi #ShefChem16 Bayesian equation presented! | |
| baoilleach #ShefChem16 Bayesian analysis: explains the eqn - likelihood of observation given posterior distribution | |
| mvkrier #ShefChem16 Ryo Yoshida (cannot decide on the twitter account to pick) from @tousuuken explores the chemical space by means of Bayesian Algo | |
| baoilleach #ShefChem16 Refers to training as forward prediction, and application of model to unknowns as backward prediction (I think) | |
| WendyAnneW #ShefChem16 fingerprint descriptors in forward predictn machine learning. Then backward prediction to get novel mols. | |
| baoilleach #ShefChem16 Working on metials, e.g. polymers (thermal conductivity, glass transition temp), solar cells, Li batteries and drugs | |
| baoilleach #ShefChem16 Data science can be an alternative to QM calcs | |
| WendyAnneW #ShefChem16 model stitching method. Ultra fast property prediction | |
| baoilleach #ShefChem16 Data production from QM -> Structure/Prop data -> Machine learning | |
| baoilleach #ShefChem16 About 10^8 small mols from PubChem, fully optimized with QM, ... | |
| baoilleach #ShefChem16 Early results on predicting HOMO-LUMO gap: deep learning (h2o), model stitching based on #CDK fingerprints | |
| WendyAnneW #ShefChem16 deep learning and model stitching compared for homo lumo gap | |
| dgelemi #ShefChem16 Deep learning! But not fully optimised. Model stitching is doing better (now explanation on the difference between ensemble) | |
| baoilleach #ShefChem16 Model stitching is about using different models for different areas of activity space (my words - I'm not familiar with this) | |
| WendyAnneW #ShefChem16 loss of info when fp made. So use ensemble learning: model stitching | |
| mvkrier #shefchem16 @ProfvLilienfeld would enjoy Yoshidasan's talk a lot.. | |
| nathanbroo #ShefChem16 looks like generating ensembles of local models that are predictive locally. | |
| baoilleach #ShefChem16 ...sorry, different models for diff areas of structural space. Use the most accurate model for a particular structure. | |
| baoilleach #ShefChem16 One dataset for training. Another for estimating the accuracy fn (weighted voting). | |
| baoilleach #ShefChem16 Kolekota-Roth ... a type of fp? | |
| dgelemi #ShefChem16 also use different descriptors as well as different models https://twitter.com/baoilleach/status/750642202091593728 | |
| baoilleach @pwk2013 #ShefChem16 very possibly - wasn't quite clear on the why | |
| baoilleach #ShefChem16 Backward prediction is about randomly sampling structures that fit to the posterior distribs | |
| baoilleach #ShefChem16 Graph mutation - restricting geometry, chemically-unfavourable bonds, and more... | |
| baoilleach #ShefChem16 Done using Chemical Language Model. Structure generation using NLP techniques to mutate SMILES string. Brackets must be matched. | |
| dgelemi #ShefChem16 natural language processing used to learn from smiles string presented. Chemical language model | |
| baoilleach #ShefChem16 More than 70% of generated molecules matched signif to ones in PubChem. | |
| baoilleach #ShefChem16 (I love the idea of randomly generating molecules - this is a novel way of doing it.) | |
| WendyAnneW #ShefChem16 paper by the student. hisaki ikebata is being reviewed. We have been asked not to photograph unpublished results | |
| baoilleach #ShefChem16 For inverse molecular design, searching for molecules with particular H-L gap and internal energy | |
| WendyAnneW #ShefChem16 software iqspr is at the CRAN repository | |
| baoilleach #ShefChem16 (aside: myself and @ghutchis have a paper on this particular topic too - we used genetic algorithm working on monomers) | |
| the_cdk Lots of CDK depictions used by Yoshida, some from the (nice) new but also the old renderings. #ShefChem16 | |
| http://cdkdepict-openchem.rhcloud.com/ | |
| baoilleach #ShefChem16 Shows scatterplot of HL gap vs E - very few in desired prop region - (wondering is the search completely random or is it directd | |
| WendyAnneW @dgelemi #ShefChem16 thanks for the CRAN url | |
| nathanbroo #ShefChem16 @baoilleach did you see this earlier this year from @Piman314 -https://medium.com/@nf508/de-novo-design-without-the-chemistry-d183e8a9f150#.l5z8zkpbq | |
| baoilleach #ShefChem16 A problem is the poor prediction of trained forward models at far tails of the distrib of existing molecules | |
| baoilleach #ShefChem16 ...but goal of mol design is to go where no-one has gone before - i.e. there won't be molecules available there to train on | |
| baoilleach #ShefChem16 ...so we put the generated data back into the model, and then do the predictions again - hopefully will improve things | |
| baoilleach #ShefChem16 Collabs with Mitsubishi-Tanabe, Harvard CEP, among others | |
| GJPvWesten @baoilleach I like the star trek reference.. :) #ShefChem16 | |
| dgelemi #ShefChem16 questions get lost in translation between German English to Japanese speaker. #speakslow #clarity | |
| WendyAnneW #ShefChem16 van westen on which method to use for modelling public data | |
| baoilleach #ShefChem16 Gerard van Westen Target prediction, QSAR, PCM, Deep learning - reliable cross target modeling of ChEMBL? | |
| baoilleach #ShefChem16 Based in Leiden, working closely with expt alists | |
| dgelemi #ShefChem16 reference to aeronautic engineering as 1st slide from @GJPvWesten | |
| nathanbroo #ShefChem16 up now: @GJPvWesten from Leiden LACDR talking about target prediction, QSAR, PCM & deep learning. | |
| baoilleach #ShefChem16 Predictions from models not found to be that reliable | |
| baoilleach #ShefChem16 Try ensembles - major vote - still had trouble | |
| WendyAnneW #ShefChem16 he wants more reliable target predictions even using networks | |
| baoilleach #ShefChem16 What we did was, to go back to ChEMBL, and investigate it more | |
| nathanbroo #ShefChem16 target prediction from models did not work well enough to use. Back to @ChEMBL… | |
| baoilleach #ShefChem16 ChEMBL size displayed - growing quickly - all good...but not perfect | |
| baoilleach #ShefChem16 On average each molecule tested on 1.5 protein targets. Sparse. Want to predict for other targets not present. | |
| WendyAnneW #ShefChem16 looked at ChEMBL. Amazing resource...but sparse matrix and there is huge matrix of drug target pairs | |
| WendyAnneW #ShefChem16 high confidence part of ChEMBL very very much smaller | |
| baoilleach #ShefChem16 When you limit to high quality pChEMBL values, there are only about 2-3% of the activity values retained | |
| baoilleach #ShefChem16 One of the problems is the 10 micromolar activity cutoff. Then 90% of the data points are active. | |
| nathanbroo #ShefChem16 a certain @georgeisyourman did *not* approve this message! @ChEMBL data difficult to model. | |
| WendyAnneW #ShefChem16 Jenkins et al on target fishing. Van westen could not get it to work. Threshold cutoff 10 micro mol not ideal | |
| baoilleach #ShefChem16 So how to model it. XKCD cartoon on standards shown. | |
| WendyAnneW #ShefChem16 he decided to use one dataset with 14 methods. | |
| WendyAnneW #ShefChem16 used 300 nm threshold | |
| baoilleach #ShefChem16 What did we do differently? Use ~300nM as activity threshold. Biologically more relevant - better sep between acts and inacts | |
| baoilleach #ShefChem16 Several ways to model a single dataset: binary class QSAR, multiclass, PCM (protochemometrics), multiclass DNN (deep learnin') | |
| WendyAnneW #ShefChem16 binary qsar, multi class qsar, PCM, multi class DNN compared | |
| baoilleach #ShefChem16 Python/Lasagne + #RDKIT, R + Pipeline Pilot | |
| baoilleach #ShefChem16 All scripts made availabale | |
| WendyAnneW #ShefChem16 hi qual dataset is available | |
| baoilleach #ShefChem16 High quality data from ChEMBL: has pCHEMBL, no data validity comment, no literature dups | |
| nathanbroo #ShefChem16 @xkcdComic used to illustrate growing standards: https://xkcd.com/927/ pic.twitter.com/ZBmx9L3ytK | |
| baoilleach #ShefChem16 Explains how the Bayes classifiers were trained | |
| WendyAnneW #ShefChem16 PLoS one 2015, 10.3: e0121492. PLoS comput biol 2013, 9:10 pp? | |
| egonwillig this is the first time I see someone compare stat modelling with standards... not intuitive. Why??? #ShefChem16 https://twitter.com/nathanbroon/status/750649600072187904 | |
| baoilleach #ShefChem16 Who has already used DNNs? Only the last speaker puts up his hand. | |
| WendyAnneW #ShefChem16 deep neural networks DNN explained | |
| WendyAnneW #ShefChem16 DNN implemented in lasagne | |
| baoilleach #ShefChem16 Feed forward NN; used dropout (25%) to prevent overtraining; 3 layers; no. of output nodes equal to no. of targets | |
| nathanbroo #ShefChem16 deep learning neural networks explained… I should get into this. Impossible to interpret or interrogate models I'm guessing. | |
| egonwillig I like that XKCD comic, but I don't think it has anything to do with selecting modeling methods... there're no standards 4 that #ShefChem16 | |
| baoilleach #ShefChem16 Validation: usually with random split of data; we use temporal split of the data; predict the future (I did this too!) | |
| WendyAnneW #ShefChem16 danger in 70 30 Data set split cos medchemists make series. temporal split of the data is more challenging | |
| WendyAnneW #ShefChem16 mcc mathews correlation coefficient | |
| baoilleach #ShefChem16 Compares random splitting and temporal validation; with temporal validation performance goes way down | |
| WendyAnneW #ShefChem16 temporal - performance goes down a lot. Best | |
| baoilleach #ShefChem16 Reason is that med chemists work in series at a time, so random splits puts some of the series in training and some in the test | |
| WendyAnneW #ShefChem16 oops best was mc DNN? | |
| baoilleach #ShefChem16 Compares run times; DNN fast if use GPUs | |
| WendyAnneW #ShefChem16 z-score also used. He likes the DNNs | |
| baoilleach #ShefChem16 In a fair comparison DNN outperform the other methods | |
| nathanbroo #ShefChem16 Bayesian models comparable to deep learning neural nets. Bayesian more interpretable? DLNN can improve with complexity? | |
| baoilleach #ShefChem16 Did a param sweep of 63 DNNs models - took only 1 week between idea and results | |
| WendyAnneW Proteochemometric DNNs = PCM DNN #ShefChem16 | |
| baoilleach #ShefChem16 Best are deep and wide nets, large pooled datasets and increased descriptors - ensembles best of all | |
| baoilleach #ShefChem16 Future work: more scope to investigate DNNs, and do expts - "can tell experimentalists - we can help you now, trust me again" | |
| WendyAnneW #ShefChem16 no single method is best. Diff methods good in diff cases. He likes DNN. PCM DNN better but slower. PCM RF bad | |
| rguha @dgelemi @WendyAnneWarr first R tool at #shefchem16? | |
| baoilleach #ShefChem16 Jameed Hussain: MOEsaic - making SAR analysis easier thru use of MMPs | |
| nathanbroo #ShefChem16 next up: Jameed Hussein @CCG_MOE talking about #MOEsaic their new tool for easier SAR analysis using Matched Molecular Pairs. | |
| WendyAnneW #ShefChem16 jameed Hussain of CCG. Making sar analysis easier thru use of matched mol pairs (MMPs). MOEsaic | |
| rguha @baoilleach klekota roth (structural keys) #shefchem16 | |
| baoilleach #ShefChem16 Many tools available, but few are integrated environments - chemists frustrated as need to know to use many tools | |
| dgelemi #ShefChem16 no mention of datawarrior as visualisation tool next to spotfire! If you don't know it, get it http://www.openmolecules.org/datawarrior/ | |
| baoilleach #ShefChem16 Interactive viz of SAR - can be a challenge - time consuming - many assays, many chemical series | |
| WendyAnneW #ShefChem16 moesaic is web-based app for sar analysis and compound design | |
| baoilleach #ShefChem16 MOEsaic - a web-based app for SAR analysis and cmpd design | |
| nathanbroo #ShefChem16 Spotfire & Vortex mentioned. Don't forget the free DataWarrior for #dataviz http://www.openmolecules.org/datawarrior/ pic.twitter.com/FmZD0pQKY5 | |
| baoilleach #ShefChem16 Explore effects of structural change, investigate if a trend is general or scaffold-dependent | |
| WendyAnneW #ShefChem16 addresses typical medchem workflows | |
| baoilleach #ShefChem16 MedChem cycle: Have data, look at it, gen hypothesis, use to design new cmpds, come together and discuss, make them, test, rpeat | |
| baoilleach #ShefChem16 Software built to match this process | |
| WendyAnneW #ShefChem16 browse design and document | |
| baoilleach #ShefChem16 Browse, design and document workflow | |
| baoilleach #ShefChem16 Example of COX-2 inhibitor: what is the effect of making a particular change? Shows a graph comparing activities of MMPs | |
| WendyAnneW #ShefChem16 what is effect of change of funct Group on activity etc. See the scaffolds and the 3D ligand-prot model | |
| nathanbroo #ShefChem16 design-make-test cycle in medicinal chemistry explained. Here's my take on it. pic.twitter.com/qWXiPqEnGI | |
| WendyAnneW #ShefChem16 compare the props , the structures , the MMPs all on one screen | |
| baoilleach #ShefChem16 Refs @DrBostrom paper on oxadiazoles as example of MMPs | |
| rguha Did the DNN use précomputed descs? Or did they learn a representation? Former may explain similar perf #shefchem16 https://twitter.com/nathanbroon/status/750651707844165632 | |
| baoilleach #ShefChem16 Other examples: BioDig (GSK), WizePairZ (AZ), FindPairs (Pfizer) | |
| baoilleach #ShefChem16 Challenge to present MMPA to make it easy enough to use for med chemists | |
| baoilleach #ShefChem16 "MMP Explore" - a mode in the software that highlight parts of the molecule where MMPs are availabale to explore | |
| baoilleach #ShefChem16 Click on the highlighted bond to see the corresponding MMPs. Background colours used to indicate property value. | |
| nathanbroo #ShefChem16 nice shiny new tool from @CCG_MOE. Can't wait to give it a try with some of our internal data. | |
| dgelemi #ShefChem16 will @nmsoftware be mentioned during the talk... | |
| baoilleach #ShefChem16 Can also look at changes in linker sizes (from double-cuts I presume) by clicking on two bonds | |
| WendyAnneW #ShefChem16 forgive me saying so, but this is a bit of a product review | |
| baoilleach #ShefChem16 Following the network of MMPs from one to another - a bit like the earlier talks on SmallWorld and Astex Fragment Network | |
| baoilleach #ShefChem16 Can map different properties to y axis to see whether the SAR is transferable, and for what scaffolds | |
| WendyAnneW #ShefChem16 R-group profile plots shown on busy slide | |
| baoilleach #ShefChem16 Can sort the molecules along x-axis to see whether particular properties highlight a trend with respect to MMPs | |
| baoilleach #ShefChem16 Can sort R groups by chemical similarity - often groups similarly behaving groups together | |
| baoilleach #ShefChem16 Explains non-transferability for particular MMP in terms of steric hindrance | |
| dgelemi #ShefChem16 smooth and lots of visualisation. But still requires a med-chemist to sit and click on what he/she wants to see. | |
| baoilleach #ShefChem16 Design module: don't want to break the flow between analysis and design - avoid "speed bumps" | |
| baoilleach #ShefChem16 Can modify structures and profile on-the-fly using visual models | |
| baoilleach #ShefChem16 When saving a novel cmpd design, can add comments on why you think good, and you don't need to go out to another tool | |
| baoilleach #ShefChem16 Document model - describe the workflow of your analysis and capture any insights - add notes and jot ideas | |
| WendyAnneW #ShefChem16 pic.twitter.com/HzjFmbTgQM | |
| baoilleach #ShefChem16 Summary: built to address typical workflows of med chemists and keep track of insights, mirrors how SAR is generated | |
| baoilleach #ShefChem16 Particular suitable for series which are not amenable to R-groups, especially at early stage | |
| WendyAnneW #ShefChem16 pic.twitter.com/ayFdnmfo2D | |
| rguha @DrJoshuaBox @nathanbroon @GJPvWesten so learned directly from molecular graph ?(e.g. neural fps https://arxiv.org/abs/1509.09292 ) #ShefChem16 | |
| baoilleach #ShefChem16 Based on ideas/experience from 12 years of GSK | |
| baoilleach #ShefChem16 The end | |
| WendyAnneW @CCG_MOE #ShefChem16 pic.twitter.com/Clu8W0jlq2 | |
| WendyAnneW #ShefChem16 not quite the end. Can have more discussions over lunch https://twitter.com/baoilleach/status/750659722949296130 | |
| egonwillig @baoilleach @WendyAnneWarr @dgelemi @nathanbroon thanks for the detailed #ShefChem16 coverage! https://twitter.com/baoilleach/status/750659722949296130 | |
| WendyAnneW Overall, papers and posters were high quality #ShefChem16 | |
| nathanbroo #ShefChem16 conference closed. See you in three years! pic.twitter.com/8848re7eVy | |
| GJPvWesten #ShefChem16 taxis did not show op so we took the 120 bus.. Adventures in #sheffield | |
| GJPvWesten #ShefChem16 taxis did not show op so we toon the 120 bus.. Adventures in #sheffield @nathanbroon @georgeisyourman @WendyAnneWarr | |
| dgelemi #shefchem16 taxi queue. Like Cambridge train station. May do like @GJPvWesten or try uberpic.twitter.com/Uzcux6t1ba | |
| dgelemi #shefchem16 been called "love" and "darling" when buying train ticket @HannahJemi @DrJoshuaBox | |
| dgelemi #shefchem16 been good to meet in person @WendyAnneWarr @nathanbroon @mvkrier @DrJoshuaBox now can put a face on tweets! | |
| mattymattm Safe journey home to all@ #shefchem16 and for any interested parties a highly recommended job prospect at Sheffield! pic.twitter.com/lD4CfTAlO0 | |
| cressetgro Didn't see our #software case studies at #ShefChem16? See them at http://bit.ly/CressCase #CompChem #VirtualScreening #SARanalysis | |
| nathanbroo #ShefChem16 Legonath having a quick pint before his train back to London. The experiment went a little wrong… pic.twitter.com/NTGGgNjQoY | |
| nathanbroo Great tweeting guys! #ShefChem16 @WendyAnneWarr @baoilleach @mvkrier @dgelemi @GJPvWesten @georgeisyourman @HannahJemi @DrJoshuaBox | |
| cressetgro Missed our #DiscoveryServices case studies at #ShefChem16? See them at http://bit.ly/CDScase #AnalyzeSAR #BridgeResourceGaps #Outsource | |
| mvkrier After #shefchem16 meeting: SAVI | |
| DrJoshuaBo Beautiful pint of mint chocolate stout @SheffieldTap to mark the end of another great #ShefChem16. That's all folks! pic.twitter.com/MNDGEqYfu0 | |
| cressetgro Missed details of our academic licensing program at #ShefChem16? See http://bit.ly/CresAcad #Teaching #Student #Research #Free #LowCost | |
| drjohndhol Have a safe journey home everyone #ShefChem16 | |
| GJPvWesten #ShefChem16 no draught beer at cafe balzar Manchester Airport, there is at bar mcr... | |
| nathanbroo #ShefChem16 remember the next UK-QSAR & #Chemoinformatics meeting is being held @ICR_London on Wed 19th October. Registration opens soon! | |
| egonwillig @WendyAnneWarr well, the flow was impressive and overwhelming! #ShefChem16 pic.twitter.com/iO6OB8Hdf6 | |
| egonwillig @egonwillighagen @baoilleach @WendyAnneWarr @dgelemi @nathanbroon it gives me a lot to catch up with! #ShefChem16 pic.twitter.com/lczl4AWiNu | |
| nathanbroo Shall we @Storify #ShefChem16? Anyone any experience? @baoilleach @WendyAnneWarr @mvkrier @dgelemi | |
| GJPvWesten @rguha also presented as poster on #ShefChem16, interesting indeed! pic.twitter.com/OmGXQpyn43 | |
| DrJoshuaBo @HannahJemi didn't have time to ask in Sheff. How are you doing protein pharmacophore in rdkit. Using a feature factory? #ShefChem16 | |
| HannahJemi @DrJoshuaBox Yes exactly. I modified a feature factory written by a previous student that only includes protein-specific smarts. #ShefChem16 | |
| baoilleach #ShefChem16 Just published the paper corresponding to my poster on "which fp is best?" http://jcheminf.springeropen.com/articles/10.1186/s13321-016-0148-0 | |
| nmsoftware #ShefChem16 Just published the paper corresponding to my poster on "which fp is best?" http://jcheminf.springeropen.com/articles/10.1186/s13321-016-0148-0 | |
| nmsoftware My #ShefChem16 poster "Which is the best fingerprint for medicinal chemistry?" #cheminformatics http://www.slideshare.net/NextMoveSoftware/which-is-the-best-fingerprint-for-medicinal-chemistry via @SlideShare | |
| baoilleach My #ShefChem16 poster "Which is the best fingerprint for medicinal chemistry?" #cheminformatics http://www.slideshare.net/NextMoveSoftware/which-is-the-best-fingerprint-for-medicinal-chemistry via @SlideShare | |
| baoilleach This was my first time live-tweeting a conference. Was my coverage of #ShefChem16... | |
| AmethystD2 Had an excellent time at the #ShefChem16 conference. Thank you for organising. Already looking forward to the next time! |
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