An Ontology‑Guided Drug–Herb–Food Interaction Checker with Mechanism‑Based Knowledge Graph Reasoning and Condition‑Aware Interpretation

Background The concurrent use of prescription medicines with herbal products, dietary supplements, and foods is common, particularly among individuals with chronic diseases. Such real-world co-consumption generates interaction patterns beyond conventional drug–drug interactions. Existing interaction-checking systems remain largely drug-centric, rely on predefined interaction pairs, and provide limited mechanistic transparency and condition-aware interpretation. Consequently, they are poorly equipped to represent interactions influenced by health-related conditions such as age, renal impairment, pregnancy, and lifestyle. Methods We developed the Drug–Herb–Food Interaction Checker (DHFI-C), an ontology-guided knowledge graph platform for mechanism-based and condition-inclusive interaction assessment. Evidence was curated from open-access literature under PRISMA 2020 and transformed into a structured data model spanning drugs, herbs, foods, health-related conditions, and underlying diseases. Entities and interaction components were aligned with external biomedical ontologies where appropriate, whereas a DHFI mini-ontology captured underrepresented interaction concepts. The model was implemented as a graph-native representation paired with a deterministic inference engine that derives pharmacokinetic and pharmacodynamic interactions through shared mechanistic pathways. We evaluated the DHFI-C using a comprehensive, predefined use case. Results The knowledge graph integrated >24,000 drug entities, 92 herb/food entities, and 1,277 curated interaction records, along with mechanistic nodes for enzymes, transporters, and pharmacodynamic effects. The DHFI-C reports both curated and mechanism-inferred interactions with explicit provenance. In the use case, the system handled multi-domain interactions, produced condition-level interpretations, detected pharmacological effect duplication, decomposed combination products, and supported disease-driven drug suggestions. Outputs are available in consumer and expert presentation modes, with mechanistic explanations. Conclusions The DHFI-C provides a transparent and extensible framework for assessing drug–herb–food interactions through integrated, mechanism-based reasoning. By modeling health-related conditions as first-class entities and unifying heterogeneous domains within a single knowledge graph, the platform addresses the key limitations of existing interaction checkers and enables context-aware, mechanism-driven interpretation.