Machine learning in drug discovery and development part 1 – a primer

AbstractArtificial 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 paper will summarize applications of ML in drug discovery, drug development, and post ‐approval phase.

Source: CPT: Pharmacometrics and Systems Pharmacology - January 5, 2020 Category: Drugs & Pharmacology Authors: Alan Talevi, Juan Francisco Morales, Gregory Hather, Jagdeep Podichetty, Sarah Kim, Peter C Bloomingdale, Samuel Kim, Jackson Burton, Joshua D Brown, Almut G Winterstein, Stephan Schmidt, J Kael White, Daniela J Conrado Tags: TUTORIAL Source Type: research