The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction

Journal of Chemical Information and Modeling
Mauro S Nogueira, Oliver Koch

Abstract

The identification of possible targets for a known bioactive compound is of the utmost importance for drug design and development. Molecular docking is one possible approach for in-silico protein target prediction, whereas a molecule is docked into several different protein structures to identify potential targets. This reverse docking approach is hampered by the limitation of current scoring functions to correctly discriminate between targets and nontargets. In this work, a development of target-specific scoring functions is described that showed improved prediction performances for the correct target prediction of both actives and decoys on three validation data sets. In contrast to pure ligand-based approaches, that are in general faster and include a greater target space, docking-based approaches can cover also unknown chemical space that lies outside the known bioactivity data. These target-specific scoring functions are based on known bioactivity data retrieved from ChEMBL and supervised machine learning approaches. Neural Networks and Support Vector Machines (SVMs) models were trained for 20 different protein targets. Our protein-ligand interaction fingerprint PADIF (Protein Atom Score Contributions Derived Interaction F...Continue Reading

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Citations

Nov 16, 2019·Journal of Computer-aided Molecular Design·Edelmiro MomanVladimir A Potemkin
Sep 7, 2019·International Journal of Molecular Sciences·Luca Pinzi, Giulio Rastelli
Dec 24, 2020·Expert Opinion on Drug Discovery·Julio Caballero
May 26, 2021·Journal of Chemical Information and Modeling·Kushal BatraSean Ekins
Aug 14, 2020·Journal of Chemical Information and Modeling·Natesh SinghBruno O Villoutreix
Jun 17, 2020·Journal of Chemical Information and Modeling·Zoe CourniaWoody Sherman
Nov 16, 2019·Current Medicinal Chemistry·Gabriela Bitencourt-FerreiraWalter Filgueira de Azevedo
Nov 18, 2021·Molecular Informatics·Mingna LiZhenming Liu

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