Drug hunters are moving into the clinic with human-first ‘no-hypothesis’ target discovery, applying the full force of machine learning to massive collections of human omics data.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Precise identification of cell states altered in disease using healthy single-cell references
Nature Genetics Open Access 12 October 2023
-
Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning
Molecular Medicine Open Access 24 January 2023
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Eisenstein, M. Machine learning powers biobank-driven drug discovery. Nat Biotechnol 40, 1303–1305 (2022). https://doi.org/10.1038/s41587-022-01457-1
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41587-022-01457-1