Machine Learning in Drug Discovery and Development Part 1: A Primer. (11th March 2020)

• Record Type: Journal Article
• Authors: Talevi, Alan; Morales, Juan Francisco; Hather, Gregory; Podichetty, Jagdeep T.; Kim, Sarah; Bloomingdale, Peter C.; Kim, Samuel; Burton, Jackson; Brown, Joshua D.; Winterstein, Almut G.; Schmidt, Stephan; White, Jensen Kael; Conrado, Daniela J.
• Is Part Of: CPT: pharmacometrics & systems pharmacology. Volume 9:Number 3(2020)
• Page Start: 129
• Page End: 142
• Publication Date: 2020-03-11
• Subjects: Pharmacokinetics -- Periodicals; Pharmacology -- Periodicals; Pharmacokinetics; Periodicals; 615.05
• DOI: 10.1002/psp4.12491
• Languages: English
• ISSNs: 2163-8306

Abstract

Abstract : 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.