MSAT: a FAERS-informed heterogeneous graph neural network for pharmacovigilance prediction of Chinese materia medica–associated adverse drug reactions

By unifying FAERS pharmacovigilance evidence with multi-scale biomedical mechanisms in a heterogeneous graph learning framework, MSAT enables robust prediction and prioritization of CMM-associated ADR risks. This framework can support hypothesis generation and risk triage for post-marketing safety surveillance of complex Chinese Materia Medica products.