{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T09:50:05Z","timestamp":1792144205646,"version":"4.4.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T00:00:00Z","timestamp":1748908800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-04596-y","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T15:32:08Z","timestamp":1748964728000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Analysis of a nonsteroidal anti inflammatory drug solubility in green solvent via developing robust models based on machine learning technique"],"prefix":"10.1038","volume":"15","author":[{"given":"Lijie","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiqing","family":"Liao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hourong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowen","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"4596_CR1","doi-asserted-by":"publisher","first-page":"101465","DOI":"10.1016\/j.jcou.2021.101465","volume":"46","author":"M Amani","year":"2021","unstructured":"Amani, M., Saadati Ardestani, N. & Majd, N. Y. Utilization of supercritical CO2 gas antisolvent (GAS) for production of capecitabine nanoparticles as anti-cancer drug: analysis and optimization of the process conditions. J. CO2 Utilization. 46, 101465 (2021).","journal-title":"J. CO2 Utilization"},{"key":"4596_CR2","doi-asserted-by":"publisher","first-page":"102831","DOI":"10.1016\/j.jcou.2024.102831","volume":"83","author":"H Kanda","year":"2024","unstructured":"Kanda, H. et al. Preparation of \u03b2-carotene nanoparticles using supercritical CO2 anti-solvent precipitation by injection of liquefied gas feed solution. J. CO2 Utilization. 83, 102831 (2024).","journal-title":"J. CO2 Utilization"},{"key":"4596_CR3","doi-asserted-by":"publisher","first-page":"106262","DOI":"10.1016\/j.supflu.2024.106262","volume":"209","author":"C Lv","year":"2024","unstructured":"Lv, C. et al. Preparation of indapamide-HP-\u03b2-CD and indapamide-PVP nanoparticles by supercritical antisolvent technology: experimental and DPD simulations. J. Supercrit. Fluids. 209, 106262 (2024).","journal-title":"J. Supercrit. Fluids"},{"key":"4596_CR4","doi-asserted-by":"publisher","first-page":"103212","DOI":"10.1016\/j.csite.2023.103212","volume":"49","author":"AJ Obaidullah","year":"2023","unstructured":"Obaidullah, A. J. Thermodynamic and experimental analysis of drug nanoparticles Preparation using supercritical thermal processing: solubility of Chlorothiazide in different Co-solvents. Case Stud. Therm. Eng. 49, 103212 (2023).","journal-title":"Case Stud. Therm. Eng."},{"key":"4596_CR5","doi-asserted-by":"publisher","first-page":"106341","DOI":"10.1016\/j.ejps.2022.106341","volume":"181","author":"X Wang","year":"2023","unstructured":"Wang, X. et al. Fabrication of betamethasone micro- and nanoparticles using supercritical antisolvent technology: in vitro drug release study and Caco-2 cell cytotoxicity evaluation. Eur. J. Pharm. Sci. 181, 106341 (2023).","journal-title":"Eur. J. Pharm. Sci."},{"key":"4596_CR6","doi-asserted-by":"publisher","first-page":"106510","DOI":"10.1016\/j.supflu.2024.106510","volume":"218","author":"H Peyrovedin","year":"2025","unstructured":"Peyrovedin, H. et al. Studying the rifampin solubility in supercritical CO2 with\/without co-solvent: Experimental data, modeling and machine learning approach. J. Supercrit. Fluids. 218, 106510 (2025).","journal-title":"J. Supercrit. Fluids"},{"key":"4596_CR7","doi-asserted-by":"publisher","first-page":"114099","DOI":"10.1016\/j.fluid.2024.114099","volume":"582","author":"P AravindKumar","year":"2024","unstructured":"AravindKumar, P. et al. New solubility model to correlate solubility of anticancer drugs in supercritical carbon dioxide and evaluation with Kruskal\u2013Wallis test. Fluid. Phase. Equilibria. 582, 114099 (2024).","journal-title":"Fluid. Phase. Equilibria"},{"key":"4596_CR8","doi-asserted-by":"publisher","first-page":"113877","DOI":"10.1016\/j.fluid.2023.113877","volume":"573","author":"M Bazaei","year":"2023","unstructured":"Bazaei, M. et al. Measurement and thermodynamic modeling of solubility of erlotinib hydrochloride, as an anti-cancer drug, in supercritical carbon dioxide. Fluid. Phase. Equilibria. 573, 113877 (2023).","journal-title":"Fluid. Phase. Equilibria"},{"key":"4596_CR9","doi-asserted-by":"publisher","first-page":"105634","DOI":"10.1016\/j.jddst.2024.105634","volume":"95","author":"SM Fazel-Hoseini","year":"2024","unstructured":"Fazel-Hoseini, S. M. et al. Modeling of drug solubility with extended Hildebrand solubility approach and jouyban-acree equations in binary and ternary solvent mixtures. J. Drug Deliv. Sci. Technol. 95, 105634 (2024).","journal-title":"J. Drug Deliv. Sci. Technol."},{"issue":"1","key":"4596_CR10","doi-asserted-by":"publisher","first-page":"103502","DOI":"10.1016\/j.arabjc.2021.103502","volume":"15","author":"A Sadeghi","year":"2022","unstructured":"Sadeghi, A. et al. Machine learning simulation of pharmaceutical solubility in supercritical carbon dioxide: prediction and experimental validation for Busulfan drug. Arab. J. Chem. 15 (1), 103502 (2022).","journal-title":"Arab. J. Chem."},{"key":"4596_CR11","doi-asserted-by":"publisher","first-page":"118120","DOI":"10.1016\/j.molliq.2021.118120","volume":"349","author":"T Wang","year":"2022","unstructured":"Wang, T. & Su, C. H. Medium Gaussian SVM, wide neural network and Stepwise linear method in Estimation of lornoxicam pharmaceutical solubility in supercritical solvent. J. Mol. Liq. 349, 118120 (2022).","journal-title":"J. Mol. Liq."},{"key":"4596_CR12","doi-asserted-by":"publisher","first-page":"103268","DOI":"10.1016\/j.csite.2023.103268","volume":"49","author":"M Zhang","year":"2023","unstructured":"Zhang, M. & Mahdi, W. A. Development of SVM-based machine learning model for estimating lornoxicam solubility in supercritical solvent. Case Stud. Therm. Eng. 49, 103268 (2023).","journal-title":"Case Stud. Therm. Eng."},{"key":"4596_CR13","doi-asserted-by":"publisher","first-page":"143508","DOI":"10.1016\/j.jclepro.2024.143508","volume":"472","author":"OE Agwu","year":"2024","unstructured":"Agwu, O. E. et al. Carbon capture using ionic liquids: an explicit data driven model for carbon (IV) oxide solubility Estimation. J. Clean. Prod. 472, 143508 (2024).","journal-title":"J. Clean. Prod."},{"issue":"5","key":"4596_CR14","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.3390\/pr11051339","volume":"11","author":"S Alatefi","year":"2023","unstructured":"Alatefi, S. et al. Integration of multiple bayesian optimized machine learning techniques and conventional well logs for accurate prediction of porosity in carbonate reservoirs. Processes 11 (5), 1339 (2023).","journal-title":"Processes"},{"key":"4596_CR15","doi-asserted-by":"publisher","first-page":"119306","DOI":"10.1016\/j.molliq.2022.119306","volume":"359","author":"WK Abdelbasset","year":"2022","unstructured":"Abdelbasset, W. K. et al. Modeling and computational study on prediction of pharmaceutical solubility in supercritical CO2 for manufacture of nanomedicine for enhanced bioavailability. J. Mol. Liq. 359, 119306 (2022).","journal-title":"J. Mol. Liq."},{"key":"4596_CR16","doi-asserted-by":"publisher","first-page":"103021","DOI":"10.1016\/j.jcou.2025.103021","volume":"92","author":"FMA Altalbawy","year":"2025","unstructured":"Altalbawy, F. M. A. et al. Universal data-driven models to estimate the solubility of anti-cancer drugs in supercritical carbon dioxide: correlation development and machine learning modeling. J. CO2 Utilization. 92, 103021 (2025).","journal-title":"J. CO2 Utilization"},{"key":"4596_CR17","doi-asserted-by":"publisher","first-page":"105241","DOI":"10.1016\/j.chemolab.2024.105241","volume":"254","author":"Y Liu","year":"2024","unstructured":"Liu, Y. et al. Machine learning based modeling for Estimation of drug solubility in supercritical fluid by adjusting important parameters. Chemometr. Intell. Lab. Syst. 254, 105241 (2024).","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"4596_CR18","doi-asserted-by":"publisher","first-page":"114720","DOI":"10.1016\/j.ejpb.2025.114720","volume":"211","author":"A Roosta","year":"2025","unstructured":"Roosta, A., Esmaeilzadeh, F. & Haghbakhsh, R. Predicting the solubility of drugs in supercritical carbon dioxide using machine learning and atomic contribution. Eur. J. Pharm. Biopharm. 211, 114720 (2025).","journal-title":"Eur. J. Pharm. Biopharm."},{"key":"4596_CR19","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.aap.2015.12.001","volume":"88","author":"Q Shi","year":"2016","unstructured":"Shi, Q., Abdel-Aty, M. & Lee, J. A bayesian ridge regression analysis of Congestion\u2019s impact on urban expressway safety. Accid. Anal. Prev. 88, 124\u2013137 (2016).","journal-title":"Accid. Anal. Prev."},{"key":"4596_CR20","doi-asserted-by":"crossref","unstructured":"Schapire, R. E. Explaining adaboost. Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, : pp. 37\u201352. (2013).","DOI":"10.1007\/978-3-642-41136-6_5"},{"key":"4596_CR21","unstructured":"Drucker, H. Improving Regressors Using Boosting Techniques. In Icml (Citeseer, 1997)."},{"issue":"7","key":"4596_CR22","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.1162\/neco.2006.18.7.1678","volume":"18","author":"DL Shrestha","year":"2006","unstructured":"Shrestha, D. L. & Solomatine, D. P. Experiments with adaboost. RT, an improved boosting scheme for regression. Neural Comput. 18 (7), 1678\u20131710 (2006).","journal-title":"Neural Comput."},{"key":"4596_CR23","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.supflu.2015.10.008","volume":"107","author":"M Banchero","year":"2016","unstructured":"Banchero, M. & Manna, L. Solubility of Fenamate drugs in supercritical carbon dioxide by using a semi-flow apparatus with a continuous solvent-washing step in the depressurization line. J. Supercrit. Fluids. 107, 400\u2013407 (2016).","journal-title":"J. Supercrit. Fluids"},{"key":"4596_CR24","doi-asserted-by":"publisher","first-page":"103249","DOI":"10.1016\/j.engappai.2019.103249","volume":"87","author":"V Hayyolalam","year":"2020","unstructured":"Hayyolalam, V. & Kazem, A. A. P. Black widow optimization algorithm: a novel meta-heuristic approach for solving engineering optimization problems. Eng. Appl. Artif. Intell. 87, 103249 (2020).","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4596_CR25","doi-asserted-by":"publisher","first-page":"105622","DOI":"10.1016\/j.engappai.2022.105622","volume":"117","author":"A Alorf","year":"2023","unstructured":"Alorf, A. A survey of recently developed metaheuristics and their comparative analysis. Eng. Appl. Artif. Intell. 117, 105622 (2023).","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"11","key":"4596_CR26","doi-asserted-by":"publisher","first-page":"1640","DOI":"10.3390\/e24111640","volume":"24","author":"C Wan","year":"2022","unstructured":"Wan, C. et al. Improved black widow spider optimization algorithm integrating multiple strategies. Entropy 24 (11), 1640 (2022).","journal-title":"Entropy"},{"key":"4596_CR27","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1023\/A:1013912006537","volume":"48","author":"M Collins","year":"2002","unstructured":"Collins, M., Schapire, R. E. & Singer, Y. Logistic regression, adaboost and Bregman distances. Mach. Learn. 48, 253\u2013285 (2002).","journal-title":"Mach. Learn."},{"key":"4596_CR28","unstructured":"Freund, Y. & Schapire, R. E. Experiments with a new boosting algorithm. in icml. Citeseer. (1996)."},{"key":"4596_CR29","doi-asserted-by":"crossref","unstructured":"Schapire, R. E. & Freund, Y. Boosting: foundations and algorithms. Kybernetes 42 (1), 164\u2013166 (2013).","DOI":"10.1108\/03684921311295547"},{"key":"4596_CR30","doi-asserted-by":"crossref","unstructured":"Marwala, T. Automatic relevance determination in economic modeling. In Economic Modeling Using Artificial Intelligence Methods 45\u201364 (Springer, London, 2013).","DOI":"10.1007\/978-1-4471-5010-7_3"},{"key":"4596_CR31","doi-asserted-by":"crossref","unstructured":"Rasmussen, C. E. & Williams, C. K. Gaussian Processes for Machine Learning Cambridge (MIT Press [Google Scholar], 2006).","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"4596_CR32","doi-asserted-by":"crossref","unstructured":"Ramentol, E., Olsson, T. & Barua, S. Machine learning models for industrial applications, in AI and Learning Systems-Industrial Applications and Future Directions. IntechOpen. (2021).","DOI":"10.5772\/intechopen.93043"},{"issue":"9","key":"4596_CR33","doi-asserted-by":"publisher","first-page":"2653","DOI":"10.3390\/en14092653","volume":"14","author":"S Alatefi","year":"2021","unstructured":"Alatefi, S. & Almeshal, A. M. A new model for Estimation of bubble point pressure using a bayesian optimized least square gradient boosting ensemble. Energies 14 (9), 2653 (2021).","journal-title":"Energies"},{"key":"4596_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jmp.2018.03.001","volume":"85","author":"E Schulz","year":"2018","unstructured":"Schulz, E., Speekenbrink, M. & Krause, A. A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions. J. Math. Psychol. 85, 1\u201316 (2018).","journal-title":"J. Math. Psychol."},{"issue":"1","key":"4596_CR35","doi-asserted-by":"publisher","first-page":"22779","DOI":"10.1038\/s41598-024-74553-8","volume":"14","author":"M Li","year":"2024","unstructured":"Li, M. et al. Optimization of drug solubility inside the supercritical CO2 system via numerical simulation based on artificial intelligence approach. Sci. Rep. 14 (1), 22779 (2024).","journal-title":"Sci. Rep."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-04596-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-04596-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-04596-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T15:32:11Z","timestamp":1748964731000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-04596-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,3]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["4596"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-04596-y","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,3]]},"assertion":[{"value":"27 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"19456"}}