Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints

Abstract

The well-known concept of quantitative structure-activity relationships (QSAR) has been gaining significant interest in the recent years. Data, descriptors, and algorithms are the main pillars to build useful models that support more efficient drug discovery processes with in silico methods. Significant advances in all three areas are the reason for the regained interest in these models. In this book chapter we review various machine learning (ML) approaches that make use of measured in vitro/in vivo data of many compounds. We put these in context with other digital drug discovery methods and present some application examples.

Keywords: Artificial intelligence (AI); Data science; Deep neural network; FAIRification; In silico ADMET; Machine learning (ML); Pharmacological endpoint; Physicochemical properties; Quantitative structure–activity relationship (QSAR).