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

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          Abstract

          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.

          Abstract

          We developed Deep Docking, a deep learning platform that relies on quantitative structure−activity relationship models trained with docking scores of small portions of ultralarge databases to predict scores of remaining entries and, thus, to accelerate virtual screening by 50 times.

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          Most cited references25

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          Conformer Generation with OMEGA: Algorithm and Validation Using High Quality Structures from the Protein Databank and Cambridge Structural Database

          Here, we present the algorithm and validation for OMEGA, a systematic, knowledge-based conformer generator. The algorithm consists of three phases: assembly of an initial 3D structure from a library of fragments; exhaustive enumeration of all rotatable torsions using values drawn from a knowledge-based list of angles, thereby generating a large set of conformations; and sampling of this set by geometric and energy criteria. Validation of conformer generators like OMEGA has often been undertaken by comparing computed conformer sets to experimental molecular conformations from crystallography, usually from the Protein Databank (PDB). Such an approach is fraught with difficulty due to the systematic problems with small molecule structures in the PDB. Methods are presented to identify a diverse set of small molecule structures from cocomplexes in the PDB that has maximal reliability. A challenging set of 197 high quality, carefully selected ligand structures from well-solved models was obtained using these methods. This set will provide a sound basis for comparison and validation of conformer generators in the future. Validation results from this set are compared to the results using structures of a set of druglike molecules extracted from the Cambridge Structural Database (CSD). OMEGA is found to perform very well in reproducing the crystallographic conformations from both these data sets using two complementary metrics of success.
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            Ultra-large library docking for discovering new chemotypes

            Despite intense interest in expanding chemical space, libraries of hundreds-of-millions to billions of diverse molecules have remained inaccessible. Here, we investigate structure-based docking of 170 million make-on-demand compounds from 130 well-characterized reactions. The resulting library is diverse, representing over 10.7 million scaffolds otherwise unavailable. The library was docked against AmpC β-lactamase and the D4 dopamine receptor. From the top-ranking molecules, 44 and 549 were synthesized and tested, respectively. This revealed an unprecedented phenolate inhibitor of AmpC, which was optimized to 77 nM, the most potent non-covalent AmpC inhibitor known. Crystal structures of this and other new AmpC inhibitors confirmed the docking predictions. Against D4, hit rates fell monotonically with docking score, and a hit-rate vs. score curve predicted 453,000 D4 ligands in the library. Of 81 new chemotypes discovered, 30 were sub-micromolar, including a 180 pM sub-type selective agonist.
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              DeepTox: Toxicity Prediction using Deep Learning

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                Author and article information

                Journal
                ACS Cent Sci
                ACS Cent Sci
                oc
                acscii
                ACS Central Science
                American Chemical Society
                2374-7943
                2374-7951
                19 May 2020
                24 June 2020
                : 6
                : 6
                : 939-949
                Affiliations
                []Vancouver Prostate Centre, University of British Columbia , Vancouver, British Columbia V6H3Z6, Canada
                []Swetox, Unit of Toxicology Sciences, Karolinska Institutet , Forskargatan 20, SE-151 36 Södertalje, Sweden
                []Department of Computer and Systems Sciences, Stockholm University , Box 7003, SE-164 07 Kista, Sweden
                Author notes
                Article
                10.1021/acscentsci.0c00229
                7318080
                32607441
                e7f8a56c-f1d4-4e50-991d-f5e3f8cb1efd
                Copyright © 2020 American Chemical Society

                This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.

                History
                : 24 February 2020
                Categories
                Research Article
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                oc0c00229

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