{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T05:02:49Z","timestamp":1789448569069,"version":"build-2803163510"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Chem."],"abstract":"<jats:p>\n                    Enhancing the dissolution behavior of poorly water-soluble pharmaceuticals remains an important challenge in drug formulation and manufacturing, since limited aqueous solubility can substantially restrict therapeutic performance. Supercritical-fluid technologies provide a promising route for addressing this limitation, particularly through the production of nanoscale drug formulations with improved dissolution characteristics. In the present work, the ternary solubility of Nystatin in the SC-CO\n                    <jats:sub>2<\/jats:sub>\n                    \u2013ethanol system was modeled as a function of temperature and pressure. Four regression approaches\u2014K-nearest neighbors, linear regression, multilayer perceptron (MLP), and Theil\u2013Sen regression\u2014were examined as individual learners and subsequently incorporated into AdaBoost-based ensemble models. Model-specific hyperparameters were tuned using Glowworm Swarm Optimization whose population-based search strategy is suitable for exploring heterogeneous optimization spaces containing different parameter types. The developed ensemble configurations demonstrated strong agreement with the experimental solubility data, as reflected by high coefficients of determination and low root mean square errors. Among all evaluated models, the AdaBoost\u2013MLP configuration provided the most favorable predictive performance, achieving an\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    of 0.99412 together with the lowest root mean square error.\n                  <\/jats:p>","DOI":"10.3389\/fchem.2026.1947884","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T04:26:16Z","timestamp":1789446376000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence modeling of small molecule pharmaceutical solubility in supercritical fluid and comparative analysis under temperature and pressure variations"],"prefix":"10.3389","volume":"14","author":[{"given":"Haolin","family":"Wu","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong","place":["Hong Kong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hadil Faris","family":"Alotaibi","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint AbdulRahman University","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed Salah","family":"Fahem","sequence":"additional","affiliation":[{"name":"College of Engineering, University of Al Maarif","place":["Al Anbar, Iraq"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed","family":"Shirazian","sequence":"additional","affiliation":[{"name":"Institute of Research and Development, Duy Tan University","place":["Da Nang, Vietnam"]},{"name":"School of Engineering & Technology, Duy Tan University","place":["Da Nang, Vietnam"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"103021","DOI":"10.1016\/j.jcou.2025.103021","article-title":"Universal data-driven models to estimate the solubility of anti-cancer drugs in supercritical carbon dioxide: correlation development and machine learning modeling","volume":"92","author":"Altalbawy","year":"2025","journal-title":"J. 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