Predicting drug response and synergy using a deep learning model of human cancer cells
Open accessAbstract
Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. We address these challenges by developing DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lines to 684 drugs. Tumor genotypes induce states in cellular subsystems that are integrated with drug structure to predict response to therapy and, simultaneously, learn biological mechanisms underlying the drug response. DrugCell predictions are accurate in cell lines and also stratify clinical outcomes. Analysis of DrugCell mechanisms leads directly to the design of synergistic drug combinations, which we validate systematically by combinatorial CRISPR, drug-drug screening in vitro, and patient-derived xenografts. DrugCell provides a blueprint for constructing interpretable models for predictive medicine.
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AI agents show significant potential in personalized treatment by leveraging patient-specific data to tailor individualized treatment plans that optimize therapeutic outcomes.
“AI agents show significant potential in the realm of personalized treatment by leveraging a variety of patient-specific data, including genetic information, medical history, lifestyle factors, and treatment responses.77,88,89,90,91,92”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [91]
The Discussion (page not specified) says DrugCell uses patients’ mutational status to generate treatment recommendations, and the Methods describe predicting treatment response from patient mutational profiles. This supports the genetic-information aspect of personalized treatment, but the paper does not establish that the model uses medical history or lifestyle factors to tailor plans, or that its recommendations optimize patient outcomes. (LLM, conf=0.88)
Experimental — accuracy not yet measuredDrugCell is an interpretable deep learning model of cancer cell proliferation trained on the response of tumor cell lines to a large panel of drugs, combining genotype and drug structure to predict drug response along with specific cellular subsystems mediating that response.
“This thread was recently extended through the development of DrugCell, an interpretable deep learning model of cancer cell proliferation (Kuenzi et al., 2020). Here, the model was trained on the response of tumor cell lines to a large panel of drugs, combining genotype and drug structure to predict the drug response along with the specific cellular subsystems mediating that response.”
From Mapping the multiscale structure of biological systems (2021) · cites this paper as Kuenzi et al., 2020
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Elmarakeby, H. A., Hwang, J., Arafeh, R., Crowdis, J., Gang, S., Liu, D., AlDubayan, S. H., Salari, K., Kregel, S., Richter, C., et al. (2021) · Nature · doi:10.1038/s41586-021-03922-4 · Cited by 456 · see citing works
Self-citationEmpowering biomedical discovery with AI agents
Gao, S., Fang, A., Huang, Y., Giunchiglia, V., Noori, A., Schwarz, J. R., Ektefaie, Y., Kondic, J., & Zitnik, M. (2024) · Cell · doi:10.1016/j.cell.2024.09.022 · Cited by 315 · see citing works
Big data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254 · see citing works
Machine learning for perturbational single-cell omics
Ji, Y., Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.016 · Cited by 137 · see citing works
Predicting and characterizing a cancer dependency map of tumors with deep learning
Chiu, Y.-C., Zheng, S., Wang, L.-J., Iskra, B. S., Rao, M. K., Houghton, P. J., Huang, Y., & Chen, Y. (2021) · Science Advances · doi:10.1126/sciadv.abh1275 · Cited by 84 · see citing works
“DL methods have also been applied to pharmacogenomics for predicting drug sensitivity and synergy (16, 17).”
MethodMapping the multiscale structure of biological systems
Schaffer, L. V., & Ideker, T. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.012 · Cited by 75 · see citing works
Self-citationAdvancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence
Duran-Frigola, M., Cigler, M., & Winter, G. E. (2023) · Journal of the American Chemical Society · doi:10.1021/jacs.2c11098 · Cited by 50 · see citing works
A foundational architecture for AI agents in healthcare
Liu, F., Niu, Y., Zhang, Q., Wang, K., Dong, Z., Wong, I. N., Cheng, L., Li, T., Duan, L., Li, K., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102374 · Cited by 41 · see citing works
- Authors
- Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J. F., Ma, J., & Ideker, T.
- Affiliations
- University of California San Diego (US)
- Purdue University West Lafayette (US)
- Corresponding author
- Ideker, Trey
- ORCID
- Venue
- Cancer Cell
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 38
- Issue
- 5
- Pages
- 672-684.e6
- ISSN
- 1535-6108 (ISSN-L)
- PMC
- PMC7737474
- OpenAlex
- W3094540299
- Cited by
- 544
- References
- 83
- Citations by year
- 2026: 100 · 2025: 105 · 2024: 113 · 2023: 107 · 2022: 61 · 2021: 55 · 2020: 2 · 2019: 1
- FWCI
- 27.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cancer Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- drug response prediction, drug synergy, combination therapy, cancer cell lines, explainable deep learning, tumor genotype, drug structure, drug repurposing, CRISPR screen, patient-derived xenografts, cancer therapy
- MeSH terms
- Deep Learning, Antineoplastic Agents, Drug Screening Assays, Antitumor, Drug Synergism, Genotype, Humans, Neoplasms, Databases, Factual, Computational Biology, Cell Line, Tumor, Patient-Specific Modeling
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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