Abstract
The high cost and attrition rate of drug development underscore the need for more effective strategies for therapeutic target discovery. Here, we present a network medicine-based machine learning framework that integrates single-cell transcriptomics, bulk multi-omic profiles, genome-wide CRISPR perturbation screens, and protein-protein interaction networks to systematically prioritise disease-specific targets. Applied to clear cell renal cell carcinoma, the framework successfully recovered established targets and predicted five therapeutic candidates, with subsequent in vitro validation demonstrating that among these, ENO2 inhibition had the strongest anti-tumour effect, followed by LRRK2, a repurposing candidate with phase III Parkinson’s disease inhibitors. The proposed approach advances target discovery by moving beyond single-feature, single-modality heuristics to a scalable, machine learning-driven strategy that is generalisable across diseases.
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Acknowledgements
The authors thank Dr. Ruoyan Li for assistance with data analysis during the early stages of this work. This research was supported by LifeArc grant (RG91966), NIHR Cambridge Biomedical Centre (BRC 1215 20014), the Cancer Research UK Cambridge Centre (RQAG/119), the National Research Foundation of Korea grant funded by the Ministry of Science and ICT (RS-2025-18362970), the Korean ARPA-H Project through the Korea Health Industry Development Institute (KHIDI) funded by the Ministry of Health and Welfare (RS-2025-25456722), and the Brain Pool Plus Fellowship Program funded by the Ministry of Science and ICT (RS-2025-25427881). G.B. was funded by Standigm. I.T. was funded by Cancer Research UK (CRUK). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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N.H. is the co-founder and Chief Technology Officer of CardiaTec Bio, a company developing therapeutics for cardiovascular diseases, and the co-founder of KURE.ai, which focuses on AI-driven oncology drug discovery. N.H. also serves on the Scientific Advisory Board of the Institute of Cancer Research (ICR). J.J. provides consultancy to Evinova on product design. All other authors declare that they have no competing interests.
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Baltušytė, G., Toleman, I.J.D., Jones, J.O. et al. A network medicine framework for multi-modal data integration in therapeutic target discovery. Commun Chem (2026). https://doi.org/10.1038/s42004-026-02049-9
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DOI: https://doi.org/10.1038/s42004-026-02049-9
