{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T14:30:30Z","timestamp":1766068230960,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,10]],"date-time":"2025-08-10T00:00:00Z","timestamp":1754784000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation (NSF)","award":["2019077"],"award-info":[{"award-number":["2019077"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Phytoecdysteroids represent a class of naturally occurring substances known for their diverse biological functions, particularly their strong ability to stimulate protein anabolism. In this study, a computational machine learning-driven quantitative structure\u2013activity relationship (QSAR) approach was applied to analyze the anabolic potential of 23 ecdysteroid compounds. The ML-based QSAR modeling was conducted using a combined approach that integrates Genetic Algorithm-based feature selection with Multiple Linear Regression Analysis (GA-MLRA). Additionally, structure optimization by semi-empirical quantum-chemical method was employed to determine the most stable molecular conformations and to calculate an additional set of structural and electronic descriptors. The most effective QSAR models for describing the anabolic activity of the investigated ecdysteroids were developed and validated. The proposed best model demonstrates both strong statistical relevance and high predictive performance. The predictive performance of the resulting models was confirmed by an external test set based on R2test values, which were within the range of 0.89 to 0.97.<\/jats:p>","DOI":"10.3390\/computation13080195","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T08:10:32Z","timestamp":1754899832000},"page":"195","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Quantitative Structure\u2013Activity Relationship Study of the Anabolic Activity of Ecdysteroids"],"prefix":"10.3390","volume":"13","author":[{"given":"Durbek","family":"Usmanov","sequence":"first","affiliation":[{"name":"Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102-7207, USA"},{"name":"Institute of the Chemistry of Plant Substances, Academy of Sciences of the Republic of Uzbekistan, Tashkent 100170, Uzbekistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3401-2379","authenticated-orcid":false,"given":"Ugiloy","family":"Yusupova","sequence":"additional","affiliation":[{"name":"Institute of the Chemistry of Plant Substances, Academy of Sciences of the Republic of Uzbekistan, Tashkent 100170, Uzbekistan"}]},{"given":"Vladimir","family":"Syrov","sequence":"additional","affiliation":[{"name":"Institute of the Chemistry of Plant Substances, Academy of Sciences of the Republic of Uzbekistan, Tashkent 100170, Uzbekistan"}]},{"given":"Gerardo M.","family":"Casanola-Martin","sequence":"additional","affiliation":[{"name":"Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102-7207, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7845-4884","authenticated-orcid":false,"given":"Bakhtiyor","family":"Rasulev","sequence":"additional","affiliation":[{"name":"Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102-7207, USA"},{"name":"Department of Chemistry, National University of Uzbekistan, Tashkent 100174, Uzbekistan"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1807","DOI":"10.1007\/s00204-019-02490-x","article-title":"Ecdysteroids as non-conventional anabolic agent: Performance enhancement by ecdysterone supplementation in humans","volume":"93","author":"Isenmann","year":"2019","journal-title":"Arch. 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