Integrating Multimodal AI with Encoded Medicinal Knowledge and Physics Simulations: A Unified Platform for Accelerated Drug Discovery from Patent Analysis to Potency Validation

Abstract Traditional drug discovery faces challenges from fragmented data, tacit knowledge dependence, and inefficient design-test cycles. We present an integrated AI workflow combining multimodal data curation, encoded medicinal chemistry rules, and physics-based simulations to establish a closed-loop from patent analysis to candidate validation. Application across six patents for four targets successfully deciphered SAR and identified representative molecules. A retrospective case study on RET kinase inhibitors demonstrated the workflow’s efficiency: focusing on patent-derived SAR enabled rapid discovery of Cpd-31 (RET IC50 = 0.48 nM, 95.88% tumor inhibition at 10 mg/kg) with only three initial compounds synthesized. This data-centric approach streamlines early drug discovery by providing objective molecular design foundations, significantly reducing synthetic efforts and hypothesis-driven exploration.