{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T19:05:43Z","timestamp":1789153543712,"version":"build-2803163510"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T00:00:00Z","timestamp":1773792000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>The limited selectivity of most catalytic-site ADAMTS-5 inhibitors and the necessity to preserve aggrecan integrity in early-grade knee osteoarthritis require the development of selective aggrecanase inhibitors. The present study conducted rational in silico screening of flavonoids as potential ADAMTS-5 inhibitors by integrating high-throughput virtual screening, molecular docking, and molecular dynamic simulations targeting the exosite domains of ADAMTS-5 (the Disintegrin-like and spacer domain). The objective was to identify plant-derived flavonoids with favorable drug-like properties and specific interactions towards the ADAMTS-5 exosite as a more targeted alternative to catalytic-site inhibition. In this study, 847 flavonoids were screened using drug-likeness and ADME criteria to identify promising leads. The top 16 selected flavonoids were further subjected to molecular docking and SAR analysis. Of these compounds, Homoeriodictyol satisfied key drug-likeness criteria and exhibited the highest binding affinity to the Disintegrin-like domain, with a binding energy of \u221223.1 kcal\/mol and favorable interactions. Molecular dynamics simulations of the Homoeriodictyol\u2013ADAMTS-5 complex over 100 ns demonstrated stable binding throughout the trajectory. DCCM analysis and PCA further supported the proposed exosite-mediated modulation. To extend exosite mapping beyond the Disintegrin-like domain, this study also examined the spacer domain using a machine-learning-predicted structural model and identified key residues that contribute to ligand binding.<\/jats:p>","DOI":"10.3390\/molecules31061016","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T14:14:05Z","timestamp":1773843245000},"page":"1016","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Computational Insights into Flavonoids for ADAMTS-5 Exosite Inhibition in Knee Osteoarthritis: Docking, MD Simulations, and Machine Learning-Guided Structure Prediction"],"prefix":"10.3390","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5481-7363","authenticated-orcid":false,"given":"Mayurakkhi","family":"Bhatia","sequence":"first","affiliation":[{"name":"Department of Digital Engineering and Artificial Intelligence, College of Science, Long Island University, Brooklyn, NY 11201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9943-0008","authenticated-orcid":false,"given":"Nithyadevi","family":"Duraisamy","sequence":"additional","affiliation":[{"name":"Department of Digital Engineering and Artificial Intelligence, College of Science, Long Island University, Brooklyn, NY 11201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammed","family":"Cherkaoui","sequence":"additional","affiliation":[{"name":"Department of Digital Engineering and Artificial Intelligence, College of Science, Long Island University, Brooklyn, NY 11201, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7603","DOI":"10.1039\/D1BM01122G","article-title":"Injectable hydrogel mediated delivery of gene-engineered adipose-derived stem cells for enhanced osteoarthritis treatment","volume":"9","author":"Yu","year":"2021","journal-title":"Biomater. 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