Machine learning classification can reduce false positives in structure-based virtual screening

Proceedings of the National Academy of Sciences of the United States of America
Yusuf O AdeshinaJohn Karanicolas

Abstract

With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter. In typical virtual screens, however, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because studies reporting new scoring methods have not validated their models prospectively within the same study. Here, we report a strategy for building a training dataset (D-COID) that aims to generate highly compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework. In retrospective benchmarks, our classifier shows outstanding performance relative to other scoring functions. In a prospective context, nearly all candidate inhibitors from a screen against acetylcholines...Continue Reading

Associated Datasets

Dec 9, 2019·Yusuf Adeshina, John Karanicolas

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Citations

Dec 22, 2020·Archives of Biochemistry and Biophysics·Claudio N Cavasotto, Juan I Di Filippo
Dec 1, 2019·Drug Discovery Today. Technologies·Pedro J Ballester
Feb 6, 2021·Scientific Reports·Isabella A GuedesMaria A Miteva
Feb 14, 2021·Journal of Hematology & Oncology·Tian ZhouPeilong Lai
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Feb 4, 2021·Bioinformatics·Fergus ImrieCharlotte M Deane
Jan 27, 2021·Journal of Chemical Information and Modeling·Reed M SteinJohn J Irwin
Mar 17, 2021·Journal of Chemical Information and Modeling·Hongyi ZhouJeffrey Skolnick
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Jun 16, 2021·Expert Opinion on Drug Discovery·AkshatKumar NigamAlán Aspuru-Guzik
Jul 16, 2021·Journal of Computer-aided Molecular Design·Hafiz Saqib AliRichard H Henchman
Jul 28, 2021·Journal of Chemical Information and Modeling·Manuel A LlanosLuciana Gavernet
Aug 18, 2021·Journal of Chemical Information and Modeling·Aayush Gupta, Huan-Xiang Zhou
Sep 11, 2021·Communications Biology·Marcelo C R MeloCesar de la Fuente-Nunez
Sep 22, 2021·Expert Opinion on Drug Discovery·Juan I Di Filippo, Claudio N Cavasotto
Sep 26, 2021·Nature Protocols·Brian J BenderBrian K Shoichet
Nov 12, 2021·Journal of Chemical Information and Modeling·Grigorii V AndrianovJohn Karanicolas

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Methods Mentioned

BETA
Assay

Software Mentioned

PLECnn
ChEMBL
Score
COID
Similarity Ensemble Approach ( SEA )
VS
XGBoost
Enamine
SwissTargetPrediction
OpenEye

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