newsNature Machine IntelligenceTrust 88 · LabPublished 21h agoLive · 1m ago
A knowledge-driven framework for predicting single-cell responses for unprofiled drugs
Nature Machine Intelligence, Published online: 26 August 2026; doi:10.1038/s42256-026-01286-w 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.
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paperExplainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature AttributionspaperPerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response PredictionpaperPredicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation RepresentationspaperCytoBERT: A Foundation Model for Cytometry DatapaperMonroe: A Molecular Foundation Model for In-Context Probabilistic Inference
Related across the graph
paperPerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response PredictionpaperMonroe: A Molecular Foundation Model for In-Context Probabilistic InferencepaperCytoBERT: A Foundation Model for Cytometry DatapaperPredicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation RepresentationspaperExplainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions