MEMES: Machine learning framework for Enhanced MolEcular Screening. Issue 35 (5th August 2021)

• Record Type: Journal Article
• Authors: Mehta, Sarvesh; Laghuvarapu, Siddhartha; Pathak, Yashaswi; Sethi, Aaftaab; Alvala, Mallika; Priyakumar, U. Deva
• Is Part Of: Chemical science. Volume 12:Issue 35(2021)
• Page Start: 11710
• Page End: 11721
• Publication Date: 2021-08-05
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/d1sc02783b
• Languages: English
• ISSNs: 2041-6520

Abstract

Abstract : A novel machine learning framework based on Bayesian optimization for efficient sampling of chemical space. The framework is able to identify 90% of top-1000 hits by only sampling 6% of the complete dataset containing ∼100 million compounds. Abstract : In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as "hits". In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.