A knowledge-driven framework for predicting single-cell responses for unprofiled drugs

Predicting cellular response to chemical perturbations is critical to build virtual cells, yet experimentally profiled compounds cover only a small fraction of this space. Existing models struggle to generalize to unprofiled compounds, as they typically treat drugs as isolated identifiers without encoding their mechanistic relationships. Here, we present MAP, a framework that integrates structured biological knowledge into cellular perturbation modelling and supports zero-shot prediction for small molecules with scarce or absent profiles. (1) We construct MAP-KG, a knowledge graph that unifies 14 public resources, spanning 187,089 drugs, 22,924 genes and 694,246 mechanistic relationships. (2) We propose a knowledge-driven pretraining strategy that aligns molecular structures, protein sequences and textual mechanistic descriptions into a unified embedding space, producing mechanism-aware and transferable gene and compound embeddings. These representations are then coupled with a pretrained single-cell foundation model to condition perturbation response prediction. (3) We evaluate MAP under two zero-shot generalization regimes: unseen cell type–drug combinations and a stricter setting of unprofiled drugs, where it improves the top-50 differentially expressed gene Pearson delta correlation by up to +12.3% and +11.8%, respectively, over the strongest baselines across three benchmarks. We further perform pathway-level functional analysis via gene set enrichment analysis for in silico screening, where MAP predicts mechanism-consistent programmes on unprofiled candidate drugs, and prioritizes four out of five approved anti-cancer drugs in A-549 (non-small-cell lung cancer). Feng et al. introduce MAP, an artificial intelligence framework that integrates biological mechanism knowledge to predict how cells respond to chemical perturbation, improving generalization to untested drugs and prioritizing cancer drug candidates in virtual screening.