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tobigithub / WDGFFVCWBZVLCE.txt
Created May 6, 2022 03:02
Inchikey collision for WDGFFVCWBZVLCE
Inchikey collision for WDGFFVCWBZVLCE
https://pubchem.ncbi.nlm.nih.gov/#query=WDGFFVCWBZVLCE
https://cactus.nci.nih.gov/cgi-bin/lookup/search
@tobigithub
tobigithub / PYSEQM-BOMD-Collab.ipynb
Created March 12, 2022 00:44
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@tobigithub
tobigithub / B2GP-PLYP.gjf
Created February 24, 2022 05:35
%nproc=4
%mem=10gb
#p b2plyp/aug-cc-pvtz iop(3/125=0360003600,3/78=0640006400,3/76=0350006500,3/77=1000010000,5/33=1)
# int(grid=ultrafine) empiricaldispersion=gd2 iop(3/174=400000)
Using the new G16 mechanism to specify nonstandard dispersion parameters // https://www.compchem.me/b2gp-plyp
0 1
C 0.0 0.0 0.0
O 0.0 0.0 1.1314
@tobigithub
tobigithub / pubchem_convert_SMILES_to_IUPAC.py
Created October 15, 2021 18:08 — forked from mbohun/pubchem_convert_SMILES_to_IUPAC.py
pubchem_convert_SMILES_to_IUPAC.py use pubchem PUG REST to get IUPAC names/strings for SMILES
import sys
import requests
from lxml import etree
if __name__=="__main__":
smiles = sys.argv[1]
html_doc = requests.get("https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/smiles/" + smiles + "/record/XML")
root = etree.XML(html_doc.text)
@tobigithub
tobigithub / source-qcxms.sh
Created August 15, 2021 02:33
How to run https://github.com/qcxms/QCxMS/
#!/bin/sh
export XTBHOME=/home/ubuntu/qcxms/.XTBPARAM
export PATH=$PATH:/home/ubuntu/qcxms
export OMP_NUM_THREADS=8
ulimit -s unlimited
echo "qcxms sourced"
@tobigithub
tobigithub / GeekBench-SweetSpotFinder-Aug21
Last active August 11, 2021 22:45
GeekBench August 2021 - SweetSpotFinder
GeekBench August 2021 - SweetSpotFinder
# Processor Specifications Score (multi) Score (single)
1 AMD Ryzen Threadripper 3990X 2.9 GHz (64 cores) 25036 1212
2 AMD Ryzen Threadripper 3970X 3.7 GHz (32 cores) 22397 1261
3 AMD Ryzen Threadripper 3960X 3.8 GHz (24 cores) 19876 1272
4 Intel Xeon W-3175X 3.1 GHz (28 cores) 19764 1081
5 Intel Xeon W-3275M 2.5 GHz (28 cores) 18880 1052
6 Intel Xeon W-3265M 2.7 GHz (24 cores) 17836 1143
7 AMD Ryzen 9 5950X 3.4 GHz (16 cores) 16755 1692
@tobigithub
tobigithub / xyz2om2.py
Created January 7, 2021 20:16 — forked from andersx/xyz2om2.py
XYZ to OM2
#!/usr/bin/env python2
import numpy as np
import sys
elements = dict()
elements["H"] = 1
elements["C"] = 6
elements["N"] = 7
elements["O"] = 8
elements["F"] = 9
@tobigithub
tobigithub / xyz2om2.f90
Created January 7, 2021 20:13 — forked from andersx/xyz2om2.f90
Stupid xyz2om2 converter in fortran (cuz it was slow to do 1M files with a python converter)
! PUBLIC DOMAIN LICENSE 2017 BY ANDERS S. CHRISTENSEN
!
! I WROTE THIS BECAUSE I WAS BORED - I DON'T RECOMMEND
! WRITING FILE PARSERS IN FORTRAN BECAUSE IT IS NOT
! PRODUCTIVE.
program convert
implicit none
@tobigithub
tobigithub / PFP-2D-SDF.sdf
Last active September 15, 2020 15:15
This file has been truncated, but you can view the full file.
Mrv1823 08262014252D
17 17 0 0 0 0 999 V2000
0.5678 1.1143 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
1.2352 0.6294 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
2.0198 0.8843 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
0.9803 -0.1553 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0
0.1553 -0.1553 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-0.3297 -0.8227 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
@tobigithub
tobigithub / retip.txt
Created May 13, 2020 22:54
Unidentified peaks remain a major problem in untargeted metabolomics by LC-MS/MS. Confidence in peak annotations increases by combining MS/MS matching and retention time. We here show how retention times can be predicted from molecular structures. Two large, publicly available datasets were used for model training in machine learning: the Fiehn hydrophilic interaction liquid chromatography dataset (HILIC) of 981 primary metabolites and biogenic amines, and the RIKEN Plant Specialized Metabolome Annotation (PlaSMA) database of 852 secondary metabolites that uses reversed-phase liquid chromatography (RPLC). Five different machine learning algorithms have been integrated into the Retip R package: the random forest, Bayesian-regularized neural network, XGBoost, light gradient-boosting machine (LightGBM) and Keras algorithms for building the retention time prediction models. A complete workflow for retention time prediction was developed in R. It can be freely downloaded from the GitHub repository (https://www.ret