{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T04:47:32Z","timestamp":1785905252771,"version":"3.56.0"},"reference-count":53,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009392","name":"Prince Sattam Bin Abdulaziz University","doi-asserted-by":"publisher","award":["PSAU\/2025\/03\/36602"],"award-info":[{"award-number":["PSAU\/2025\/03\/36602"]}],"id":[{"id":"10.13039\/100009392","id-type":"DOI","asserted-by":"publisher"}]},{"award":["PSAU\/2025\/03\/36602"],"award-info":[{"award-number":["PSAU\/2025\/03\/36602"]}],"id":[{"id":"https:\/\/ror.org\/04jt46d36","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceuticals"],"abstract":"<jats:p>Background: HER2 is a key oncogenic gene in breast cancer, involved in tumor progression, metastasis, and therapeutic resistance. This study aimed to find new HER2 inhibitors using a hybrid of machine learning (ML) and structure-based virtual screening (VS), combined with molecular dynamics (MD) simulations on various scaffolds. Methods: Four supervised molecular fingerprint classification models were trained on a dataset of 10,000 validated compounds from ChEMBL. Random Forest was the top model for screening a large compound library. Selected compounds underwent molecular docking in the HER2 ATP binding site, ADMET, drug likeness, toxicity analysis, and 200 ns MD simulations. Methods like PCA, FEL, hydrogen-bond analysis, DCCM, RDF, salt-bridge analysis, and MM\/PBSA were used to assess binding stability. Results: Virtual screening identified three compounds, CHMEBL193865 (Lead-1), CHMEBL46740 (Lead-2), and CHMEBL151318 (Lead-3)\u2014with better binding affinity and interaction profiles than the reference inhibitor. MD simulations showed stable protein\u2013ligand complexes with RMSD values of 2.32\u20132.76 \u00c5. Among these, Lead-2 was the most structurally stable, and Lead-1 had the most favorable binding free energy. All three compounds showed good drug likeness, ADMET properties, and low predicted toxicity. Conclusions: These findings support further in vitro and in vivo testing for developing new therapeutics against HER2-overexpressing breast cancer, highlighting two scaffolds with promising lead optimization potential.<\/jats:p>","DOI":"10.3390\/ph19081190","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T13:53:31Z","timestamp":1785333211000},"page":"1190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning-Driven Discovery of Novel HER2 Inhibitors Through Integrated Virtual Screening and Molecular Dynamics Simulations"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9353-6653","authenticated-orcid":false,"given":"Alhumaidi B.","family":"Alabbas","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4140-7320","authenticated-orcid":false,"given":"Safar M.","family":"Alqahtani","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Chemistry, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Smolarz, B., Nowak, A.Z., and Romanowicz, H. 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