ProteinTalks: AI-powered virtual cell model predicts drug responses from perturbation proteomics

Researchers generated over 38 million time-resolved proteomic measurements from systematically perturbed breast cancer cell lines and built ProteinTalks, a scalable virtual cell model that learns transferable dynamic representations from proteome trajectories to predict drug efficacy and synergy, discover new drug combinations, probe proteins linked to drug resistance, and stratify patient responses. The model shows robust transferability to patient-derived organoids and clinical biopsies, generally outperforming benchmark methods, and data/code are publicly available to enable in silico drug discovery.