Open AccessMachine Learning in Drug Discovery and Development Part 1: A Primer
Author(s) -
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.
Publication year - 2020
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12491
Subject(s) - drug discovery , drug development , pillar , computer science , key (lock) , artificial intelligence , drug , machine learning , data science , engineering , medicine , bioinformatics , pharmacology , computer security , biology , structural engineering
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.