Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success. Please also cite the original publication if this data is used in your work: Ianevski, A., Nader, K., Driva, K., Senkowski, W., Bulanova, D., Moyano-Galceran, L., Ruokoranta, T., Kuusanmäki, H., Ikonen, N., Sergeev, P., Vähä-Koskela, M., Giri, A. K., Vähärautio, A., Kontro, M., Porkka, K., Pitkänen, E., Heckman, C. A., Wennerberg, K., & Aittokallio, T. (2024). Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones. Nature communications, 15(1), 8579. https://doi.org/10.1038/s41467-024-52980-5