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Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity

Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity

Authors :
Maciej Wójcikowski
Pawel Siedlecki
Pedro J. Ballester
Institute of Biochemistry and Biophysics PAS, Ul. Pawinskiego 5A, 02-106 Warsaw
Institute of Biochemistry and Biophysics [Warsaw] (IBB)
Centre de Recherche en Cancérologie de Marseille (CRCM)
Aix Marseille Université (AMU)-Institut Paoli-Calmettes
Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)
Institute of Biochemistry and Biophysics
Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes
Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU)
Source :
Methods in Molecular Biology, Methods in Molecular Biology, 2053, pp.1-12, 2019, ⟨10.1007/978-1-4939-9752-7_1⟩, Methods in Molecular Biology ISBN: 9781493997510
Publication Year :
2019
Publisher :
HAL CCSD, 2019.

Abstract

International audience; Molecular docking enables large-scale prediction of whether and how small molecules bind to a macromolecular target. Machine-learning scoring functions are particularly well suited to predict the strength of this interaction. Here we describe how to build RF-Score, a scoring function utilizing the machine-learning technique known as Random Forest (RF). We also point out how to use different data, features, and regression models using either R or Python programming languages.

Details

Language :
English
ISBN :
978-1-4939-9751-0
ISBNs :
9781493997510
Database :
OpenAIRE
Journal :
Methods in Molecular Biology, Methods in Molecular Biology, 2053, pp.1-12, 2019, ⟨10.1007/978-1-4939-9752-7_1⟩, Methods in Molecular Biology ISBN: 9781493997510
Accession number :
edsair.doi.dedup.....8535853dc7680f336d3f64459454e2b0
Full Text :
https://doi.org/10.1007/978-1-4939-9752-7_1⟩