Use of machine learning approaches for novel drug discovery

• Published in: Expert opinion on drug discovery Vol. 11; no. 3; p. 225
• Main Authors: Lima, Angélica Nakagawa, Philot, Eric Allison, Trossini, Gustavo Henrique Goulart, Scott, Luis Paulo Barbour, Maltarollo, Vinícius Gonçalves, Honorio, Kathia Maria
• Format: Journal Article
• Language: English
• Published: England 03-03-2016
• Subjects: Binding Sites; Decision Trees; Drug Design; Drug Discovery - methods; Humans; Ligands; Machine Learning; Models, Biological; Molecular Docking Simulation; Neural Networks (Computer); Support Vector Machine; SBDD; LBDD; Machine Learning; Drug Design
• ISSN: 1746-045X
• DOI: 10.1517/17460441.2016.1146250

The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can be applied in several steps of the drug discovery methodology, such as prediction of target structure, prediction of biological activity of new ligands through model construction, discovery or optimization of hits, and construction of models that predict the pharmacokinetic and toxicological (ADMET) profile of compounds.
This article presents an overview on some applications of ML techniques in drug design. These techniques can be employed in ligand-based drug design (LBDD) and structure-based drug design (SBDD) studies, such as similarity searches, construction of classification and/or prediction models of biological activity, prediction of secondary structures and binding sites docking and virtual screening.
Successful cases have been reported in the literature, demonstrating the efficiency of ML techniques combined with traditional approaches to study medicinal chemistry problems. Some ML techniques used in drug design are: support vector machine, random forest, decision trees and artificial neural networks. Currently, an important application of ML techniques is related to the calculation of scoring functions used in docking and virtual screening assays from a consensus, combining traditional and ML techniques in order to improve the prediction of binding sites and docking solutions.