UCEC: A Knowledge Graph-Assisted Machine Learning for Molecular-Mechanism-Interpretable Natural Product-Disease Association Prioritization

This is the authors' abstract. We don't add key points for this paper.

Natural products (NPs) have been considered a critical source of therapeutic drugs, but prioritizing NP-disease associations remain challenging because supporting evidence is distributed across multiple ingredients, targets, protein interactions, and pathways. Herein, we propose a knowledge graph-assisted machine learning model: Uncertainty-Calibrated Evidence-Chains (UCEC) for prioritizing NP-disease associations. UCEC separates predicted association strength from the evidence-conditioned stability of local mechanistic support. On a proxy benchmark designed to recover graph-supported indirect associations, evidence-chain integration increased Hits@50 from 0.392 to 0.509, with 68.8% of NPs indicating improved per-NP AUPRC. Source-separated validation using HERB 2.0 clinical trial records further supported candidate prioritization. Thus, UCEC provides a reliability-aware approach for prioritizing graph-supported NP-disease candidates and generating traceable mechanistic hypotheses for subsequent validation.