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      An Open Drug Discovery Competition: Experimental Validation of Predictive Models in a Series of Novel Antimalarials

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          Abstract

          <p class="first" id="d5018708e396">The Open Source Malaria (OSM) consortium is developing compounds that kill the human malaria parasite, Plasmodium falciparum, by targeting PfATP4, an essential ion pump on the parasite surface. The structure of PfATP4 has not been determined. Here, we describe a public competition created to develop a predictive model for the identification of PfATP4 inhibitors, thereby reducing project costs associated with the synthesis of inactive compounds. Competition participants could see all entries as they were submitted. In the final round, featuring private sector entrants specializing in machine learning methods, the best-performing models were used to predict novel inhibitors, of which several were synthesized and evaluated against the parasite. Half possessed biological activity, with one featuring a motif that the human chemists familiar with this series would have dismissed as "ill-advised". Since all data and participant interactions remain in the public domain, this research project "lives" and may be improved by others. </p>

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          Applications of machine learning in drug discovery and development

          Drug discovery and development pipelines are long, complex and depend on numerous factors. Machine learning (ML) approaches provide a set of tools that can improve discovery and decision making for well-specified questions with abundant, high-quality data. Opportunities to apply ML occur in all stages of drug discovery. Examples include target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials. Applications have ranged in context and methodology, with some approaches yielding accurate predictions and insights. The challenges of applying ML lie primarily with the lack of interpretability and repeatability of ML-generated results, which may limit their application. In all areas, systematic and comprehensive high-dimensional data still need to be generated. With ongoing efforts to tackle these issues, as well as increasing awareness of the factors needed to validate ML approaches, the application of ML can promote data-driven decision making and has the potential to speed up the process and reduce failure rates in drug discovery and development.
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            A Deep Learning Approach to Antibiotic Discovery

            Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.
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              The Chemistry Development Kit (CDK): An Open-Source Java Library for Chemo-and Bioinformatics

              The Chemistry Development Kit (CDK) is a freely available open-source Java library for Structural Chemo-and Bioinformatics. Its architecture and capabilities as well as the development as an open-source project by a team of international collaborators from academic and industrial institutions is described. The CDK provides methods for many common tasks in molecular informatics, including 2D and 3D rendering of chemical structures, I/O routines, SMILES parsing and generation, ring searches, isomorphism checking, structure diagram generation, etc. Application scenarios as well as access information for interested users and potential contributors are given.
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                Author and article information

                Contributors
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                Journal
                Journal of Medicinal Chemistry
                J. Med. Chem.
                American Chemical Society (ACS)
                0022-2623
                1520-4804
                November 25 2021
                November 08 2021
                November 25 2021
                : 64
                : 22
                : 16450-16463
                Affiliations
                [1 ]School of Pharmacy, University College London, London WC1N 1AX, U.K.
                [2 ]Exscientia Ltd., The Schrödinger Building, Oxford Science Park, Oxford OX4 4GE, U.K.
                [3 ]Drug Discovery Unit, Division of Biological Chemistry and Drug Discovery, School of Life Sciences, University of Dundee, Dundee DD1 5EH, U.K.
                [4 ]Department of Informatics, Faculty of Natural and Mathematical Sciences, King’s College London, London WC2B 4BG, U.K.
                [5 ]Molomics, Barcelona Science Park, Barcelona 08028, Spain
                [6 ]Intellegens Ltd., Eagle Labs, Chesterton Road, Cambridge CB4 3AZ, U.K.
                [7 ]Theory of Condensed Matter Group, Cavendish Laboratories, University of Cambridge, Cambridge CB3 0HE, U.K.
                [8 ]Auromind Ltd, 126 Eglantine Avenue, Belfast BT9 6EU, U.K.
                [9 ]School of Medical Sciences, The University of Sydney, Sydney, NSW 2006, Australia
                [10 ]Optibrium Ltd. Blenheim House, Denny End Road, Cambridge CB25 9QE, U.K.
                [11 ]Research School of Biology, Australian National University, Canberra, ACT 2601, Australia
                [12 ]Kellerberrin, 6 Wharf Rd, Balmain, Sydney, NSW 2041, Australia
                [13 ]School of Chemistry, The University of Sydney, Sydney, NSW 2006, Australia
                [14 ]Department of Biochemistry and Molecular Biophysics, Kansas State University, Manhattan Kansas 66506, United States
                [15 ]Strathclyde Institute Of Pharmacy And Biomedical Sciences, University of Strathclyde, Glasgow G4 ORE, U.K.
                [16 ]Interlinked Therapeutics LLC, Portland, Oregon 97214, United States
                [17 ]Medicines for Malaria Venture, PO Box 1826, 20 rte de Pre-Bois, 1215 Geneva 15, Switzerland
                Article
                10.1021/acs.jmedchem.1c00313
                34748707
                b2112b43-304d-476c-a0ed-5e46baf479fd
                © 2021

                https://creativecommons.org/licenses/by/4.0/

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