Neural interaction explainable AI predicts drug response across cancers.

ABSTRACT: Personalized treatment selection is crucial for cancer patients due to the high variability in drug response. While actionable mutations can increasingly inform treatment decisions, most therapies still rely on population-based approaches. Here, we introduce neural interaction explainable AI (NeurixAI), an explainable and highly scalable deep learning framework that models drug-gene interactions and identifies transcriptomic patterns linked with drug response. Trained on data from 546 646 drug perturbation experiments involving 1135 drugs and molecular profiles from 476 tumors, NeurixAI accurately predicted treatment responses for 272 targeted and 30 chemotherapeutic drugs in unseen tumor samples (Spearman's rho >0.2), maintaining high performance on an external validation set. Additionall

SUBMITTER: Keyl P 

PROVIDER: S-EPMC12409417 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature