A growing cadre of startups is pursuing iterative cycles of machine learning, wet-lab experimentation and human feedback to accelerate target drug discovery.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Eisenstein, M. Active machine learning helps drug hunters tackle biology. Nat Biotechnol 38, 512–514 (2020). https://doi.org/10.1038/s41587-020-0521-4
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41587-020-0521-4
- Springer Nature America, Inc.
This article is cited by
-
Characterizing emerging companies in computational drug development
Nature Computational Science (2024)
-
Structural insights into inhibition of PRRSV Nsp4 revealed by structure-based virtual screening, molecular dynamics, and MM-PBSA studies
Journal of Biological Engineering (2022)
-
Recent advances in the microbial production of isopentanol (3-Methyl-1-butanol)
World Journal of Microbiology and Biotechnology (2021)