A Nature Communications study unveiled a deep learning framework that integrates biologically informed drug representations to predict breast cancer responses across drug panels and molecular profiles. The model combines chemical, genomic, and pathway features to improve prediction of efficacy and to highlight actionable drug–tumor interactions. Authors Ge, Mo, Wei and colleagues validated the approach on multiple datasets and showed improved treatment stratification versus baseline models. For pharma and translational teams, the method offers a scalable way to prioritize drug candidates, design combination strategies, and refine biomarker hypotheses prior to costly clinical testing. The study underscores growing adoption of mechanistic AI to translate multi‑omic signals into therapeutic insight.