Physics-inspired explainable AI for mechanistic and decision support in drug discovery

• Physics-inspired explainable artificial intelligence links prediction to mechanistic insight, improves plausibility and testability of molecular hypotheses and enables decision support in lead optimization and toxicity. • Decision-centric metrics assess practical utility. • Molecular intelligence frames decision-driven workflows. Explainable artificial intelligence (XAI) is increasingly recognized as essential for trustworthy drug discovery, yet many approaches remain post hoc and correlational. This review examines physics-inspired XAI as a framework for improving mechanistic interpretability and decision support at the molecular design stage. We highlight its strengths in modeling molecular recognition and local structure–property relations, while emphasizing that efficacy and toxicity emerge from biological and pharmacological processes beyond molecular physics. Current evidence suggests that explainability improves prioritization and experimental efficiency, although its impact on overall R&D outcomes remains limited. We propose ‘molecular intelligence’ as a decision-centric framework integrating prediction, validation and domain knowledge.