Journal article
Machine learning in preclinical drug discovery
Abstract
Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%. Machine learning (ML) presents an opportunity to improve the drug-discovery process. Indeed, with the growing abundance of public and private large-scale biological and chemical datasets, ML techniques are becoming well positioned as useful tools that …
Authors
Catacutan DB; Alexander J; Arnold A; Stokes JM
Journal
Nature Chemical Biology, Vol. 20, No. 8, pp. 960–973
Publisher
Springer Nature
Publication Date
August 2024
DOI
10.1038/s41589-024-01679-1
ISSN
1552-4450