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

Abstract

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 p = 0.0002). The highest-ranked formulations showed their greatest stable-signature overlap within a distinct RAAS/vascular receptor-associated module, whereas proteins from the ion-channel/excitability-associated module were sparsely represented across the formulation networks. Blind protein-identity attribution using an identity-aware graph convolutional network provided internal cross-model consistency: 10 of the 15 highest GCN-attributed proteins overlapped the stable SVM signature, compared with 2.58 expected by chance (p = 4.78 × 10−6), and formed a densely connected candidate subnetwork (empirical p = 0.0009). Importantly, the framework is agnostic to the direction of pharmacological action; therefore, high-ranking formulations indicate molecular resemblance to the antihypertensive reference space rather than predicted blood-pressure-lowering efficacy. These findings distinguish a strongly disease-discriminative ion-channel network from a receptor-associated molecular interface more frequently represented within the curated formulation network. The framework provides mechanistically organized hypotheses for experimental validation rather than evidence of clinical antihypertensive efficacy.