{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:07:16Z","timestamp":1792789636571,"version":"4.5.0"},"reference-count":62,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation of Korea","award":["RS-2026-25476642"],"award-info":[{"award-number":["RS-2026-25476642"]}],"id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"crossref"}]},{"award":["RS-2026-25476642"],"award-info":[{"award-number":["RS-2026-25476642"]}],"id":[{"id":"https:\/\/ror.org\/013aysd81","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Mol. Sci.","IJMS"],"abstract":"<jats:p>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\u2013protein 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 \u00b1 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 &lt; 0.05, although eight proteins reached exploratory q &lt; 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 \u00d7 10\u22126), 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.<\/jats:p>","DOI":"10.3390\/ijms27198548","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T13:26:11Z","timestamp":1790256371000},"page":"8548","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Drug-Referenced Machine-Learning Framework for Prioritizing Korean Herbal Formulations in Hypertension Using Linear SVM and Graph Convolutional Networks"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3179-9439","authenticated-orcid":false,"given":"Dong-Woo","family":"Lim","sequence":"first","affiliation":[{"name":"Department of Diagnostics, College of Korean Medicine, Dongguk University, Goyang 10326, Republic of Korea"},{"name":"Institute of Korean Medicine, Dongguk University, Goyang 10326, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,24]]},"reference":[{"key":"ref_1","first-page":"157","article-title":"Hypertension in the Modern Era: Pathophysiology, Updated Guidelines, and Evidence-Based Management Strategies","volume":"1","author":"Pulatov","year":"2026","journal-title":"Int. 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