A pretrained unified model enables cellular functional profile prediction and multi-objective virtual drug screening

Authors

Chen, R., Huang, L., Qiao, Y., Mandal, S., Mo, L., Li, L., Leshchiner, D., Zhang, X., Pu, J., Xie, Y., Girgis, R., Ellsworth, E., Huang, L., Chen, X., Li, X., Zhou, J., Chen, B.

Abstract

Cells are characterized by molecular states, coordinated molecular interactions, regulatory programs, and responses to perturbations. Systematic mapping of these cellular functional profiles across biological contexts remains experimentally costly and fragmented. Here we present InsilicoCell, a pretrained multi-modal, multi-task model that unifies prediction of cellular functional profiles spanning molecular states, molecular interactions, and perturbation-induced responses. Built on a supervised transformer architecture and pretrained on more than 88 million measurements across seven tasks, including drug sensitivity, drug-induced gene expression, and drug-protein binding, InsilicoCell learns a shared representation that links molecular profiles to cellular phenotypes, improves performance over task-specific models, and generalizes to unseen entities, contexts, and conditions. InsilicoCell extends beyond cell line systems to patient, spatial and single-cell settings, and enables multi-objective virtual drug screening. It identifies novel candidate compounds with experimental validation, including c-Myc activity inhibitors, antifibrotic agents and stemness-inducing compounds. Together, InsilicoCell provides a scalable framework for predictive cellular biology and therapeutic discovery.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 29 Aug 2026.

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