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

Title:
Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity.
Authors:
Wójcikowski M; Institute of Biochemistry and Biophysics PAS, Warsaw, Poland., Siedlecki P; Institute of Biochemistry and Biophysics PAS, Warsaw, Poland.; Department of Systems Biology, Institute of Experimental Plant Biology and Biotechnology, University of Warsaw, Warsaw, Poland., Ballester PJ; Cancer Research Center of Marseille, INSERM U1068, Marseille, France. pedro.ballester@inserm.fr.; Institut Paoli-Calmettes, Marseille, France. pedro.ballester@inserm.fr.; Aix-Marseille Université, Marseille, France. pedro.ballester@inserm.fr.; CNRS UMR7258, Marseille, France. pedro.ballester@inserm.fr.
Source:
Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2019; Vol. 2053, pp. 1-12.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Humana Press Country of Publication: United States NLM ID: 9214969 Publication Model: Print Cited Medium: Internet ISSN: 1940-6029 (Electronic) Linking ISSN: 10643745 NLM ISO Abbreviation: Methods Mol Biol Subsets: MEDLINE
Imprint Name(s):
Publication: Totowa, NJ : Humana Press
Original Publication: Clifton, N.J. : Humana Press,
MeSH Terms:
Machine Learning* , Models, Molecular* , Quantitative Structure-Activity Relationship*, Databases, Genetic ; Ligands ; Protein Binding ; Proteins/chemistry ; Software ; Web Browser ; Workflow
Contributed Indexing:
Keywords: Binding affinity; Docking; Machine learning; Scoring function
Substance Nomenclature:
0 (Ligands)
0 (Proteins)
Entry Date(s):
Date Created: 20190828 Date Completed: 20200619 Latest Revision: 20200619
Update Code:
20250114
DOI:
10.1007/978-1-4939-9752-7_1
PMID:
31452095
Database:
MEDLINE

Weitere Informationen

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.