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An operational perturbation proteomics-based virtual cell model

Published: 09/09/2026, 12:00 AM

Nature (2026) Cite this article Artificial intelligence-empowered virtual cell models represent an emerging approach for in silico drug discovery1,2,3, yet most existing approaches lack large-scale, time-resolved perturbation proteomics data and interpretable frameworks for predicting therapeutic responses. Here we generated more than 38 million temporal protein-abundance measurements from systematically perturbed breast cancer cell lines, and developed ProteinTalks, a virtual cell model. Central to ProteinTalks is the synergy of this large-scale dynamic proteomic resource and the model architecture, enabling a new pretraining framework that learns transferable dynamical latent representations from temporal proteome trajectories. By modelling how proteins respond conditionally to different perturbations, this approach enables the model to function as an operational tool for diverse drug discovery tasks: predicting drug efficacy and synergy, discovering new drug combinations, probing proteins associated with drug resistance, stratifying patient responses and prioritizing drug candidates for patient organoids. It also shows robust transferability, extending beyond cell lines to patient-derived organoids and clinical biopsies, generally achieving higher performance than the selected benchmark implementations under the evaluated protocols. Together, ProteinTalks shows how scalable pretraining of transferable dynamic representations enables operational, dynamics-aware, proteomics-based virtual cell models to advance in silico drug discovery. This is a preview of subscription content, access via your institution Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription Prices may be subject to local taxes which are calculated during checkout Raw mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium available via iProX (IPX0007409000). The PTDS protein matrix and associated resources are available for academic and non-commercial use through the open-access platform db.prottalks.com. Other data used are available from the Kyoto Encyclopedia of Genes and Genomes pathway at https://www.genome.jp/kegg/pathway.html (ref. 57), from Metascape at https://metascape.org/gp/ (ref. 58), from STRING at https://cn.string-db.org/ (ref. 59) and from Ingenuity Pathway Analysis at http://www.ingenuity.com (ref. 60). The project data analysis codes are available via GitHub at https://github.com/guomics-lab/PTV-1. Bunne, C. et al. How to build the virtual cell with artificial intelligence: priorities and opportunities. Cell 187, 7045–7063 (2024). Article CAS PubMed PubMed Central Google Scholar