Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery

By Francesco Gentile, V. Agrawal, Michael Hsing, Anh-Tien Ton, Fuqiang Ban, U. Norinder, M. Gleave, Artem Cherkasov. ACS Central Science, 2020

348 citations recorded by the source corpus.

Drug discovery is a rigorous process that requires billion dollars of investments and decades of research to bring a molecule “from bench to a bedside”. While virtual docking can significantly accelerate the process of drug discovery, it ultimately lags the current rate of expansion of chemical databases that already exceed billions of molecular records. This recent surge of small molecules availability presents great drug discovery opportunities, but also demands much faster screening protocols. In order to address this challenge, we herein introduce Deep Docking (DD), a novel deep learning platform that is suitable for docking billions of molecular structures in a rapid, yet accurate fashion. The DD approach utilizes quantitative structure–activity relationship (QSAR) deep models trained on docking scores of subsets of a chemical library to approximate the docking outcome for yet unprocessed entries and, therefore, to remove unfavorable molecules in an iterative manner. The use of DD methodology in conjunction with the FRED docking program allowed rapid and accurate calculation of docking scores for 1.36 billion molecules from the ZINC15 library against 12 prominent target proteins and demonstrated up to 100-fold data reduction and 6000-fold enrichment of high scoring molecules (without notable loss of favorably docked entities). The DD protocol can readily be used in conjunction with any docking program and was made publicly available.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/8082368cd1b49fd84c28d7746820cbb8663e520b

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Citations in the corpus

Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery (2022, methodology): “Model predictions based on the following rescoring functions were ensembled in sequence: RF-Score, NNScore, PLEC score, and RF-Score-VS.”
Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking (2022, positive): “To enable structure-based screening of billion-sized molecular libraries, we recently developed DD, a technique that iteratively trains deep neural networks (DNNs) with small batches of explicitly docked compounds to infer the ranking of the yet-unprocessed remainder of the library 10 .”