Knowledge Graphs for Mechanism-Guided Natural Product Therapeutics: Integrating Compounds, Targets, Diseases, Pathways, Evidence, and Safety Signals

Download PDF

Natural product therapeutics represent a chemically diverse and biologically complex discovery space in which compounds, plant sources, targets, diseases, pathways, evidence records, and safety signals are often distributed across disconnected data resources. This article proposes a mechanism-guided knowledge graph architecture for organizing these heterogeneous data into a transparent, queryable, and validation-aware framework for natural product therapeutic research. The architecture emphasizes normalized compound, target, gene, protein, disease, phenotype, pathway, evidence, and safety entities, with relation types designed to distinguish curated associations, experimental findings, computational predictions, literature-derived statements, database-derived records, and graph-inferred hypotheses. The proposed approach places evidence provenance and safety-signal integration at the same architectural level as compound–target and disease-pathway reasoning, preventing graph connectivity from being interpreted as proof of mechanism, efficacy, or safety. Query and inference logic are framed around graph traversal, rule-based reasoning, ontology-aware inference, graph embeddings, link prediction, graph neural networks, uncertainty assessment, and expert review. The main contribution is a layered architecture that supports mechanism-guided candidate prioritization while preserving interpretability, evidence traceability, and validation requirements. For natural product therapeutics, such a framework can help organize phytochemical evidence, generate compound–target–disease hypotheses, examine pathway plausibility, screen for safety concerns, and plan translational validation. Future work should focus on reproducible graph construction, natural product entity normalization, evidence-weighting strategies, safety-aware inference, benchmark development, and experimental validation workflows that connect computational hypotheses with pharmacology, toxicology, and translational research.