Health AI Central
Peer-Reviewed Publication
Bioinformatics2026;42(6)June 1, 2026Journal Article

Drug response profile-based machine learning enables strategic cell line and compound selection for drug development.

Abbi Abdel-Rehim1, Emma Tate2, Larisa N Soldatova3, Ross D King1,4,5,6
1Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, CB3 0AS, United Kingdom.
2Arctoris Ltd, Abingdon, OX14 4SA, United Kingdom.
3Department of Mathematics, University College London, London, WC1H 0AY, United Kingdom.
4Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden.
5Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, 412 96, Sweden.
6The Alan Turing Institute, London, NW1 2DB, United Kingdom.
PubMed PMC DOI PDF

Abstract

MOTIVATION: Early-stage drug discovery relies on testing compounds across a limited set of cell lines, making it challenging to capture biological diversity while maintaining experimental efficiency. Current predictive approaches for identifying responsive cell lines often depend on high-dimensional omics data, which can be costly and difficult to interpret. We therefore evaluated whether drug-res…

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