A Drug-Referenced Machine-Learning Framework for Prioritizing Korean Herbal Formulations in Hypertension Using Linear SVM and Graph Convolutional Networks.

Hypertension is regulated by interacting molecular systems, providing a rationale for network-based screening of multicomponent therapeutic candidates. Grounded in the conceptual framework of network pharmacology, we developed a hypertension-focused computational framework to prioritize insurance-covered Korean herbal formulations using protein-protein interaction networks derived from marketed antihypertensive and comparator drugs. Thirty reference-drug graphs (15 antihypertensive and 15 comparator drugs) were evaluated using four molecular classifiers under repeated group-aware nested cross-validation. Among them, a linear support vector machine (SVM) integrating protein presence, hypertension-specific disease weight, and normalized degree achieved the highest performance (AUROC, 0.956 ± 0.007) and was applied to rank 56 herbal formulations. Across the 56 formulations, 229 unique representative compounds were retained after deduplication and used to construct formulation-specific molecular networks. The resulting protein-level SVM interpretation identified 110 directionally stable proteins associated with discrimination of the antihypertensive reference set from comparator drugs. No individual protein met strict permutation-calibrated q < 0.05, although eight proteins reached exploratory q < 0.10. Network analysis revealed a 30-protein ion-channel/excitability-associated module with the strongest aggregate model effect (empirical