{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T02:32:27Z","timestamp":1788834747102,"version":"build-2803163510"},"reference-count":87,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Zhejiang Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["LY19B060002"],"award-info":[{"award-number":["LY19B060002"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>Despite the widespread use of vaccines against SARS-CoV-2, COVID-19 continues to pose global health challenges, requiring efficient drug screening and repurposing strategies. This study presents a novel hybrid framework that integrates deep learning (DL) with molecular docking to accelerate the identification of potential therapeutics. The framework comprises three crucial steps: (1) a previously developed DL model is employed to rapidly screen candidate compounds, selecting those with predicted interaction scores above a cut-off value of 0.8; (2) AutoDock Vina version 1.5.6 and LeDock version 1.0 are used to evaluate binding affinities, with a threshold of &lt;\u22127.0 kcal\u00b7mol\u22121; and (3) predicted drug\u2013protein binding sites are evaluated to determine their overlap with known active residues of the target protein. We first validated the framework using four experimentally confirmed COVID-19 drug\u2013target pairs and then applied it to identify potential inhibitors of the SARS-CoV-2 main protease (MPro). Among 29 drug candidates selected based on antiviral, anti-inflammatory, or anti-cancer properties, only Enasidenib met all three selection criteria, showing promise as an MPro inhibitor. However, further experimental and clinical studies are required to confirm its efficacy against SARS-CoV-2. This work provides an interpretable strategy for virtual screening and drug repurposing, which can be readily adapted to other DL models and docking tools.<\/jats:p>","DOI":"10.3390\/molecules30163409","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T13:28:22Z","timestamp":1755523702000},"page":"3409","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["An Interpretable Deep Learning and Molecular Docking Framework for Repurposing Existing Drugs as Inhibitors of SARS-CoV-2 Main Protease"],"prefix":"10.3390","volume":"30","author":[{"given":"Juan","family":"Huang","sequence":"first","affiliation":[{"name":"School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialong","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4390-0303","authenticated-orcid":false,"given":"Qu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e453","DOI":"10.1016\/S1473-3099(23)00815-0","article-title":"Persistent SARS-CoV-2 infection: Significance and implications","volume":"24","author":"Machkovech","year":"2024","journal-title":"Lancet Infect. 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