Practical considerations for active machine learning in drug discovery.

TitlePractical considerations for active machine learning in drug discovery.
Publication TypeJournal Article
Year of Publication2019
AuthorsD Reker
JournalDrug Discovery Today: Technologies
Volume32-33
Start Page73
Pagination73 - 79
Date Published12/2019
Abstract

Active machine learning enables the automated selection of the most valuable next experiments to improve predictive modelling and hasten active retrieval in drug discovery. Although a long established theoretical concept and introduced to drug discovery approximately 15 years ago, the deployment of active learning technology in the discovery pipelines across academia and industry remains slow. With the recent re-discovered enthusiasm for artificial intelligence as well as improved flexibility of laboratory automation, active learning is expected to surge and become a key technology for molecular optimizations. This review recapitulates key findings from previous active learning studies to highlight the challenges and opportunities of applying adaptive machine learning to drug discovery. Specifically, considerations regarding implementation, infrastructural integration, and expected benefits are discussed. By focusing on these practical aspects of active learning, this review aims at providing insights for scientists planning to implement active learning workflows in their discovery pipelines.

DOI10.1016/j.ddtec.2020.06.001
Short TitleDrug Discovery Today: Technologies