Mining Chemogenomic Spaces for Prediction of Drug–Target Interactions

Abstract

The pipeline of drug discovery consists of a number of processes; drug–target interaction determination is one of the salient steps among them. Computational prediction of drug–target interactions can facilitate in reducing the search space of experimental wet lab-based verifications steps, thus considerably reducing time and other resources dedicated to the drug discovery pipeline. While machine learning-based methods are more widespread for drug–target interaction prediction, network-centric methods are also evolving. In this chapter, we focus on the process of the drug–target interaction prediction from the perspective of using machine learning algorithms and the various stages involved for developing an accurate predictor. © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024.