Machine Learning in Drug Discovery and Development Part 1: A Primer
Alan Talevi, Juan Francisco Morales, Gregory Hather, Jagdeep T. Podichetty, Sarah Kim, Peter Bloomingdale, Samuel Kim, Jackson Burton, Joshua D. Brown, Almut G. Winterstein, Stephan Schmidt, Jensen Kael White, Daniela J. Conrado
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.
Topics & Concepts
Drug discoveryDrug developmentComputer sciencePillarKey (lock)Artificial intelligenceDrugPrimer (cosmetics)Machine learningData scienceEngineeringMedicineBioinformaticsPharmacologyChemistryComputer securityBiologyStructural engineeringOrganic chemistryComputational Drug Discovery MethodsGenetics, Bioinformatics, and Biomedical ResearchCell Image Analysis Techniques