Machine Learning in Drug Discovery and Development Part 1: A Primer

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.

CPT: Pharmacometrics & Systems Pharmacology

Wiley-Blackwell

Año: 2020 vol. 9 p. 129 - 142

2163-8306

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, andpostapproval phase.