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https://github.com/llnl/smallmoleval

Using machine learning to score potential drug candidates may offer an advantage over traditional imprecise scoring functions because the parameters and model structure can be learned from the data. However, models may lack interpretability, are often overfit to the data, and are not generalizable to drug targets and chemotypes not in the training data. Benchmark datasets are prone to artificial enrichment and analogue bias due to the overrepresentation of certain scaffolds in experimentally determined active sets. Datasets can be evaluated using spatial statistics to quantify the dataset topology and better understand potential biases. Dataset clumping comprises a combination of self-similarity of actives and separation from decoys in chemical space and is associated with overoptimistic virtual screening results. This code explores methods of quantifying potential biases and examines some common benchmark datasets.
https://github.com/llnl/smallmoleval

machine-learning python statistics

Last synced: 24 days ago
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Using machine learning to score potential drug candidates may offer an advantage over traditional imprecise scoring functions because the parameters and model structure can be learned from the data. However, models may lack interpretability, are often overfit to the data, and are not generalizable to drug targets and chemotypes not in the training data. Benchmark datasets are prone to artificial enrichment and analogue bias due to the overrepresentation of certain scaffolds in experimentally determined active sets. Datasets can be evaluated using spatial statistics to quantify the dataset topology and better understand potential biases. Dataset clumping comprises a combination of self-similarity of actives and separation from decoys in chemical space and is associated with overoptimistic virtual screening results. This code explores methods of quantifying potential biases and examines some common benchmark datasets.

Awesome Lists containing this project

README 

        
SmallMolEval

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Using machine learning to score potential drug candidates may offer an advantage over traditional imprecise scoring functions because the parameters and model structure can be learned from the data. However, models may lack interpretability, are often overfit to the data, and are not generalizable to drug targets and chemotypes not in the training data. Benchmark datasets are prone to artificial enrichment and analogue bias due to the overrepresentation of certain scaffolds in experimentally determined active sets. Datasets can be evaluated using spatial statistics to quantify the dataset topology and better understand potential biases. Dataset clumping comprises a combination of self-similarity of actives and separation from decoys in chemical space and is associated with overoptimistic virtual screening results. This code explores methods of quantifying potential biases and examines some common benchmark datasets.



Documentation

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File: 

remove_AVE_bias2.py

slight modification on atomwise script to split data



run_remove_AVE_bias.py

example of running remove_AVE_bias2.py on DUDE dataset



main.py,main_activeonly.py,main.old.py

scripts that run the MUV spatial statistics



DescriptorSets.py

mostly contains functions used by MUV statistics and called in main files



gf.plot

plots MUV statistics



makegraphs.py

uses gf.plots to make whole dataset plots



analyze_AVE_bias.py

no revisions from atomwise, computes the bias score and AUC of ligand based models



aveanalyze.py

runs analyze_AVE_bias.py for directory of multiple directories containing splits on different receptors



Authors

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SmallMolEval was written by Dr. Sally Ellingson.



Release

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SmallMolEval is released under an MIT license.  For more details see the

NOTICE and LICENSE files.



``LLNL-CODE-759342``