AI-powered virtual cells set to forecast how biology reacts to perturbations
Researchers are building ‘virtual cells’—AI- and mechanistic-models trained on large transcriptomic and other omics datasets—to simulate cellular states and predict how cells respond to different perturbations, with the goal of speeding hypothesis generation and identifying therapeutic avenues. While foundations models and data platforms (like scBaseCount, Pisces, X-Atlas, and State) show promise for predicting perturbation effects and immune-cell behavior, experts caution that current models mainly capture static states, struggle with dynamic evolution and tissue-scale mapping, and remain limited by noisy data and a need for causal, diverse training data to achieve robust, translatable predictions.