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      Exploring differential evolution for inverse QSAR analysis

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          Abstract

          Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new methodology for coordinate optimization, termed differential evolution (DE), that originated from computer science and engineering. Using simulation and compound activity data, we demonstrate that DE in combination with support vector regression (SVR) yields effective and robust predictions of optimized coordinates satisfying model constraints and requirements. For different compound activity classes, optimized coordinates are obtained that exclusively map to regions of high activity in feature space, represent novel positions for structure generation, and are chemically meaningful.

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          Most cited references33

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          A tutorial on support vector regression

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            New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays.

            This report describes a number of substructural features which can help to identify compounds that appear as frequent hitters (promiscuous compounds) in many biochemical high throughput screens. The compounds identified by such substructural features are not recognized by filters commonly used to identify reactive compounds. Even though these substructural features were identified using only one assay detection technology, such compounds have been reported to be active from many different assays. In fact, these compounds are increasingly prevalent in the literature as potential starting points for further exploration, whereas they may not be.
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              Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges

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                Author and article information

                Contributors
                Role: Data CurationRole: Formal AnalysisRole: InvestigationRole: MethodologyRole: Writing – Review & Editing
                Role: ConceptualizationRole: SupervisionRole: Writing – Review & Editing
                Role: ConceptualizationRole: Formal AnalysisRole: SupervisionRole: Writing – Original Draft Preparation
                Journal
                F1000Res
                F1000Res
                F1000Research
                F1000Research
                F1000Research (London, UK )
                2046-1402
                6 September 2017
                2017
                : 6
                : Chem Inf Sci-1285
                Affiliations
                [1 ]Department of Chemical System Engineering, School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan
                [2 ]Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Bonn, D-53113, Germany
                [1 ]Relay Therapeutics, Cambridge, MA, USA
                [1 ]Modeling & Informatics, Vertex Pharmaceuticals, Boston, MA, USA
                [1 ]R&D IDD / Structure, Design & Informatics, Sanofi-Aventis Deutschland GmbH, Frankfurt, Germany
                Author notes

                Competing interests: No competing interests were disclosed.

                Competing interests: Prof Bajorath and I are Gateway Advisors for the channel in which the article is published.

                Competing interests: No competing interests were disclosed.

                Competing interests: No competing interests were disclosed.

                Author information
                https://orcid.org/0000-0002-0557-5714
                Article
                10.12688/f1000research.12228.2
                5580410
                28928936
                3d81a83e-aa52-4983-93a0-96d76358c155
                Copyright: © 2017 Miyao T et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 7 September 2017
                Funding
                Funded by: Japan Society for the Promotion of Science
                Award ID: 16J05325
                The project leading to this report has received funding (for TM) from the Japan Society for the Promotion of Science (JSPS) under the JSPS KAKENHI Grant Number 16J05325.
                The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Articles
                Bioinformatics
                Macromolecular Chemistry

                chemical space,active compounds,differential evolution,support vector regression,virtual screening,inverse qsar

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