https://github.com/rdkit/benchmarking_platform

https://github.com/rdkit/benchmarking_platform

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• Host: GitHub
• URL: https://github.com/rdkit/benchmarking_platform
• Owner: rdkit
• Created: 2013-07-03T06:41:03.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2022-12-19T16:47:27.000Z (about 2 years ago)
• Last Synced: 2024-09-26T02:01:39.473Z (3 months ago)
• Language: Python
• Size: 76 MB
• Stars: 26
• Watchers: 6
• Forks: 20
• Open Issues: 4
• Metadata Files:
  • Readme: README.md

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README

        
Benchmarking Platform

=====================

original version presented in 

S. Riniker, G. Landrum, J. Cheminf., 5, 26 (2013),

DOI: 10.1186/1758-2946-5-26,

URL: http://www.jcheminf.com/content/5/1/26

extended version presented in

S. Riniker, N. Fechner, G. Landrum, J. Chem. Inf. Model., 53, 2829, (2013),

DOI: 10.1021/ci400466r,

URL: http://pubs.acs.org/doi/abs/10.1021/ci400466r

GENERAL USAGE NOTES

-------------------

The virtual-screening process implemented by the benchmarking

platform is divided into three steps:

1) Scoring

2) Validation

3) Analysis

The three steps are run separately and read in the output of the

previous step. In the scoring step, the data from the directories

compounds and query_lists is read in.

The directory compounds contains lists of compounds for 118 targets

from three public data sources: MUV, DUD and ChEMBL. The compound

lists contain the external ID, the internal ID and the SMILES of

each compound.

There are three subsets of targets available:

subset I: 

  88 targets from MUV, DUD & ChEMBL described in J. Cheminf., 5, 26 (2013)

  

subset I filtered: 

  69 targets from MUV, DUD & ChEMBL filtered for difficulty

  described in JCIM (2013), online

  

subset II:

  37 targets from ChEMBL designed for a second VS use case

  described in JCIM (2013), online

The directory query_lists contains training lists for each target

with the indices of randomly selected active and inactive molecules.

Training lists with 5, 10 or 20 active molecules are available.

The number of training decoys is 20 % of the decoys for subsets I

and 10 % for subset II.

The scripts are written in Python and use the open-source

cheminformatics library RDKit (www.rdkit.org) and

machine-learning library scikit-learn (www.scikit-learn.org).

Running a script with the option [--help] gives a description of the 

required and optional input parameters of the script.