Knowledge Graph Construction for Systematic Discovery of Bioactive Compounds and Therapeutic Targets in Natural Products
Keywords:
knowledge graphs; natural products; bioactive compounds; therapeutic target discovery; data integration; semantic interoperability; fairness; infrastructureAbstract
The discovery of bioactive compounds and therapeutic targets from natural products remains a central challenge in biomedical research because the relevant evidence is scattered across heterogeneous chemical, genomic, pharmacological, and clinical data sources. This paper examines the construction and use of knowledge graphs as an integrative systems infrastructure for systematic discovery in natural product research. Rather than proposing a single algorithm, the paper develops a system-level account of how knowledge graph architectures can reconcile chemical structure, species provenance, bioactivity, protein interactions, pathway membership, disease associations, and phenotypic outcomes within one queryable semantic fabric. We analyze the structural trade-offs among schema-centric, property-graph, and linked-data designs, and we discuss entity resolution, relation extraction, provenance modeling, and quality assurance as governance challenges rather than purely technical tasks. The paper further addresses scalability, updating, deployment, fairness, and regulatory implications when such graphs support target prioritization and compound repurposing. Throughout, we emphasize that the value of a knowledge graph lies not only in inference accuracy but also in its capacity to make evidence transparent, contestable, and maintainable across research communities. We argue that sustainable knowledge graph infrastructures for natural products require a balance between automated extraction and expert curation, between open interoperability and local control, and between exploratory discovery and rigorous validation. The discussion offers forward-looking perspectives on how these systems can support more reproducible, equitable, and policy-aware natural product drug discovery.
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