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Machine learning in preclinical drug discovery
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