Machine Learning in Drug Discovery and Development Part 1: A Primer - Alan Talevi; Juan Francisco Morales; Gregory Hather; Jagdeep T. Podichetty; Sarah Kim; Peter C. Bloomingdale; Samuel Kim; Jackson Burton; Joshua D. Brown; Almut G. Winterstein; Stephan Schmidt; Jensen Kael White; et al. (Wiley, 2020)
Artificial intelligence, in particular machine learning (ML), has emerged as a key promising pillar to overcome the high failure rate in drug development. Here, we present a primer on the ML algorithms most commonly used in drug discovery and development. We also list possible data sources, describe good practices for ML model development and validation, and share a reproducible example. A companion article will summarize applications of ML in drug discovery, drug development, and postapproval phase.