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.
© 2020 The Authors. CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of the American Society for Clinical Pharmacology and Therapeutics.