George Papadatos madgpap

## desc.txt
We describe how Python is leveraged to streamline the modelling of drug discovery data and the development of tools for the scientific community. We look at various examples, e.g. chemistry toolkits, machine-learning applications and web frameworks and show how Python can glue it all together to create efficient data science pipelines.

## gist:3912ac679d6842a79f22

      
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                madgpap
                / gist:3912ac679d6842a79f22
            
            
              Last active
              August 29, 2015 14:19
                — forked from mnowotka/gist:e8130b64d1336dee7dc1
            
          
    ChEMBL is the largest open access database resource in the fields of computational drug discovery, chemoinformatics and chemical biology. Contrary to the common Perl-related perception, the Python programming language is used predominantly in the aforementioned fields. In this presentation, we describe how Python is used as the cornerstone and foundation inside and outside the ChEMBL group, in order to support and streamline many facets of our work, tools and resources. In particular, we cover the following topics:

Data modelling using Object Relational Mapping
Chemistry-specific computations using cheminformatics toolkits
Biological target prediction using machine learning tools
Improving accessibility by creating user-friendly appli


## map1
zgrep AHJRHEGDXFFMBM-UHFFFAOYSA-N SureChEMBL_map_through_2014.txt.gz | cut -f 5,6 | sort | uniq

## Target_Publications
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## patentome
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## target_prediction_demo
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## predict_GS5759
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## rdkit_inchi_roundtrip
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## MDS
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## myChEMBL_tutorial.ipynb

      
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                madgpap
                / myChEMBL_tutorial.ipynb
            
            
              Created
              November 27, 2013 09:56
            
              
                A basic tutorial of RDKit and Pandas in myChEMBL
              
          
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