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In this study, a bioactive steroidal compound, 5\u03b1-cholesta-9(11)-en-3\u03b2,20\u03b2-diol, was successfully isolated from the starfish\n                    <jats:italic>Acanthaster planci<\/jats:italic>\n                    . Structural elucidation was achieved using HREIMS, FTIR, and advanced 1D\/2D NMR spectroscopy, confirming a molecular formula of C\u2082\u2087H\u2084\u2086O\u2082 and characteristic functionalities including hydroxyl and double bond moieties. The compound demonstrated notable anti-SARS-CoV-2 activity, attaining 85% viral inhibition at 5 ng\/\u00b5l with an IC\u2085\u2080 of 5.86 \u00b5M, as demonstrated by plaque reduction assays. Molecular docking studies demonstrated significant binding affinities toward key viral targets Mpro, NSP10, and RNA-dependent RNA polymerase with free binding energies of -26.85, -27.59, and \u2212\u200935.08\u00a0kcal\/mol, respectively. These affinities surpassed those of their respective co-crystallized reference ligands. In-silico ADMET profiling indicated favorable pharmacokinetic properties, including high BBB penetration, moderate intestinal absorption, and non-hepatotoxicity. Toxicity assessments predicted low carcinogenic risk, a high rat MTD, and minimal ocular and dermal irritancy. Additionally, we developed a predictive web application based on machine learning to estimate IC\u2085\u2080 values of SARS-CoV-2 inhibitors, streamlining the drug discovery process. The forecasted values nearly matched the experimental outcomes, demonstrating the model\u2019s reliability and its potential to reduce time, cost, and risk in early-stage drug development. Moreover, machine learning models, particularly XGBoost, demonstrated excellent performance in predicting pIC\u2085\u2080 values (RMSE\u2009=\u20090.1357, MAE\u2009=\u20090.1022), supporting the development of a web-based IC\u2085\u2080 prediction application. The bioactivity prediction platform ENHPCG further validated the compound\u2019s antiviral potential, estimating an IC\u2085\u2080 of 5.95 \u00b5M. Overall, these integrated analytical, biological, and computational approaches highlight 5\u03b1-cholesta-9(11)-en-3\u03b2,20\u03b2-diol as a potential SARS-CoV-2 inhibitor and a candidate for further pharmacological development.\n                  <\/jats:p>","DOI":"10.1038\/s41598-025-20443-6","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T19:40:28Z","timestamp":1759866028000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Analytical and machine learning approaches identify a sea star steroid with promising activity for COVID-19 therapeutic development"],"prefix":"10.1038","volume":"15","author":[{"given":"Mohamed S. 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