{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T05:17:19Z","timestamp":1788585439291,"version":"build-2803163510"},"reference-count":53,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Science and Technology Program","award":["JCYJ20250604150048016"],"award-info":[{"award-number":["JCYJ20250604150048016"]}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20240813152313019"],"award-info":[{"award-number":["JCYJ20240813152313019"]}]},{"DOI":"10.13039\/501100001809","name":"The Natural Science Foundation of China","doi-asserted-by":"crossref","award":["82374531"],"award-info":[{"award-number":["82374531"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shenzhen High-level Hospital Construction Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Curr. Issues Mol. Biol.","CIMB"],"abstract":"<jats:p>Background: Hepatocellular carcinoma (HCC) remains a leading cause of cancer mortality. The c-Met and VEGFR2 pathways synergistically drive HCC progression. Marine natural products offer chemically diverse drug reservoirs; however, conventional activity-guided isolation faces labor intensity, low throughput, and frequent compound rediscovery, limiting marine drug development. Objective: To pioneer an artificial intelligence-driven marine drug discovery workflow integrating deep learning virtual screening for identifying dual c-Met\/VEGFR2 promising in silico candidate from marine natural product repositories. Methods: UniSite predicted binding pockets in c-Met (PDB: 4R1V) and VEGFR2 (PDB: 2XIR). Drug-likeness filtering of 695,000 compounds from COCONUT and CMNPD databases yielded 84,730 candidates. DiffDock-based screening identified dual-target binders, validated through 200 ns molecular dynamics simulations, MM-GBSA calculations, and DFT analyses. Results: The marine phthalide CMNPD30506 [(S)-3-ethyl-5,6-dihydroxyphthalide] emerged as the lead candidate, engaging VEGFR2 via four hydrophobic contacts and one \u03c0-cation interaction with LYS868, while binding c-Met through four hydrophobic interactions, two hydrogen bonds, and \u03c0-\u03c0 stacking. Molecular dynamics demonstrated stable RMSD profiles and dynamic hydrogen bond enrichment. MM-GBSA revealed binding free energies of \u221214.79 and \u221213.28 kcal\/mol for VEGFR2 and c-Met, respectively, driven by van der Waals forces. DFT calculations indicated a HOMO-LUMO gap of 2.410 eV. Conclusions: This AI-augmented workflow successfully identified CMNPD30506 as a promising dual c-Met\/VEGFR2 HCC therapeutic from marine libraries, overcoming traditional discovery bottlenecks through integrated deep learning and physics-based simulations, exemplifying AI\u2019s potential in marine pharmacological research.<\/jats:p>","DOI":"10.3390\/cimb48090902","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T11:08:53Z","timestamp":1788433733000},"page":"902","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Powered Marine Drug Discovery: A Putative Dual c-Met\/VEGFR2 Lead Candidate for Hepatocellular Carcinoma via Deep Learning and Multiscale Simulation"],"prefix":"10.3390","volume":"48","author":[{"given":"Ruiqi","family":"Zhao","sequence":"first","affiliation":[{"name":"The Fourth Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen 518033, 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