{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:36:34Z","timestamp":1787567794676,"version":"build-2736575974"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":66,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah Bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2023R205"],"award-info":[{"award-number":["PNURSP2023R205"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-58509-8","type":"journal-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T03:24:14Z","timestamp":1781839454000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Utilization of advanced machine learning models for analysis of pharmaceutical cocrystals by prediction of solubility parameters using Dragonfly algorithm optimization"],"prefix":"10.1038","volume":"16","author":[{"given":"Hadil Faris","family":"Alotaibi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tareq Nayef","family":"AlRamadneh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahendihasan S.","family":"Heera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anupam","family":"Yadav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed Kareem","family":"Shakir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T.","family":"Ramachandran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Swati","family":"Mishra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Navin Kumar","family":"Tailor","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Divya","family":"Singhal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,19]]},"reference":[{"issue":"8","key":"58509_CR1","doi-asserted-by":"publisher","first-page":"2537","DOI":"10.1016\/j.apsb.2021.03.030","volume":"11","author":"M Guo","year":"2021","unstructured":"Guo, M. et al. Pharmaceutical cocrystals: A review of preparations, physicochemical properties and applications. Acta Pharm. Sinica B. 11 (8), 2537\u20132564 (2021).","journal-title":"Acta Pharm. Sinica B"},{"key":"58509_CR2","doi-asserted-by":"crossref","unstructured":"Asgarpour Khansary, M., Shirazian, S. & Walker, G. A molecularly enhanced proof of concept for targeting cocrystals at molecular scale in continuous pharmaceuticals cocrystallization. Proc. Natl. Acad. Sci. 119(21), e2114277119 (2022).","DOI":"10.1073\/pnas.2114277119"},{"key":"58509_CR3","doi-asserted-by":"crossref","unstructured":"Dong, Y. et al. Data-driven modeling methods and techniques for pharmaceutical processes. Processes. 11(7), 2096 (2023).","DOI":"10.3390\/pr11072096"},{"key":"58509_CR4","doi-asserted-by":"publisher","first-page":"101034","DOI":"10.1016\/j.coche.2024.101034","volume":"45","author":"H Chang","year":"2024","unstructured":"Chang, H. et al. Bayesian data-driven models for pharmaceutical process development. Curr. Opin. Chem. Eng. 45, 101034 (2024).","journal-title":"Curr. Opin. Chem. Eng."},{"issue":"6","key":"58509_CR5","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1111\/jphp.12376","volume":"67","author":"C Loschen","year":"2015","unstructured":"Loschen, C. & Klamt, A. Solubility prediction, solvate and cocrystal screening as tools for rational crystal engineering. J. Pharm. Pharmacol. 67 (6), 803\u2013811 (2015).","journal-title":"J. Pharm. Pharmacol."},{"key":"58509_CR6","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.trac.2012.07.012","volume":"40","author":"DS Cao","year":"2012","unstructured":"Cao, D. S. et al. Tree-based ensemble methods and their applications in analytical chemistry. TRAC Trends Anal. Chem. 40, 158\u2013167 (2012).","journal-title":"TRAC Trends Anal. Chem."},{"key":"58509_CR7","doi-asserted-by":"crossref","unstructured":"Cutler, A., Cutler, D. R. & Stevens, J. R. Tree-based methods. In High-Dimensional Data Analysis in Cancer Research, 1\u201319 (Springer, 2008).","DOI":"10.1007\/978-0-387-69765-9_5"},{"issue":"1","key":"58509_CR8","doi-asserted-by":"publisher","first-page":"29970","DOI":"10.1038\/s41598-025-12886-8","volume":"15","author":"TN Alharby","year":"2025","unstructured":"Alharby, T. N. & Huwaimel, B. Machine learning analysis of pharmaceutical cocrystals solubility parameters in enhancing the drug properties for advanced pharmaceutical manufacturing. Sci. Rep. 15 (1), 29970 (2025).","journal-title":"Sci. Rep."},{"key":"58509_CR9","doi-asserted-by":"publisher","first-page":"105219","DOI":"10.1016\/j.chemolab.2024.105219","volume":"253","author":"WA Mahdi","year":"2024","unstructured":"Mahdi, W. A. & Obaidullah, A. J. Combination of machine learning and COSMO-RS thermodynamic model in predicting solubility parameters of coformers in production of cocrystals for enhanced drug solubility. Chemometr. Intell. Lab. Syst. 253, 105219 (2024).","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"58509_CR10","doi-asserted-by":"crossref","unstructured":"Li, C. et al. Machine learning approach to predict Hansen solubility parameters of cocrystal coformers via integrating group contribution and COSMO-RS. J. Mol. Liq. 125319 (2024).","DOI":"10.1016\/j.molliq.2024.125319"},{"key":"58509_CR11","doi-asserted-by":"crossref","unstructured":"Najman, K. & Zieli\u0144ski, K. Outlier detection with the use of isolation forests. In Data Analysis and Classification: Methods and Applications 29 (Springer, 2021).","DOI":"10.1007\/978-3-030-75190-6_5"},{"key":"58509_CR12","doi-asserted-by":"crossref","unstructured":"Liu, F. T., Ting, K. M. & Zhou, Z. H. Isolation forest. In Eighth IEEE International Conference on Data Mining (IEEE, 2008).","DOI":"10.1109\/ICDM.2008.17"},{"key":"58509_CR13","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Zou, C. & Dong, J. Outlier detection using isolation forest and local outlier factor. In Proceedings of the Conference on Research in Adaptive and Convergent Systems (2019).","DOI":"10.1145\/3338840.3355641"},{"key":"58509_CR14","doi-asserted-by":"crossref","unstructured":"Jovi\u0107, A., Brki\u0107, K. & Bogunovi\u0107, N. A review of feature selection methods with applications. In 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) (IEEE, 2015).","DOI":"10.1109\/MIPRO.2015.7160458"},{"issue":"11\u201313","key":"58509_CR15","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1016\/S0167-8655(99)00083-5","volume":"20","author":"P Somol","year":"1999","unstructured":"Somol, P. et al. Adaptive floating search methods in feature selection. Pattern Recognit. Lett. 20 (11\u201313), 1157\u20131163 (1999).","journal-title":"Pattern Recognit. Lett."},{"issue":"22","key":"58509_CR16","doi-asserted-by":"publisher","first-page":"7542","DOI":"10.3390\/s21227542","volume":"21","author":"BAS Emambocus","year":"2021","unstructured":"Emambocus, B. A. S. et al. Dragonfly algorithm and its hybrids: A survey on performance, objectives and applications. Sensors 21 (22), 7542 (2021).","journal-title":"Sensors"},{"key":"58509_CR17","doi-asserted-by":"publisher","first-page":"16625","DOI":"10.1007\/s00521-020-04866-y","volume":"32","author":"Y Meraihi","year":"2020","unstructured":"Meraihi, Y. et al. Dragonfly algorithm: A comprehensive review and applications. Neural Comput. Appl. 32, 16625\u201316646 (2020).","journal-title":"Neural Comput. Appl."},{"key":"58509_CR18","doi-asserted-by":"publisher","first-page":"1053","DOI":"10.1007\/s00521-015-1920-1","volume":"27","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili, S. Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27, 1053\u20131073 (2016).","journal-title":"Neural Comput. Appl."},{"key":"58509_CR19","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L. Random forests. Mach. Learn. 45, 5\u201332 (2001).","journal-title":"Mach. Learn."},{"issue":"14","key":"58509_CR20","doi-asserted-by":"publisher","first-page":"245","DOI":"10.21786\/bbrc\/13.14\/57","volume":"13","author":"B Talekar","year":"2020","unstructured":"Talekar, B. & Agrawal, S. A detailed review on decision tree and random forest. Biosci. Biotechnol. Res. Commun. 13 (14), 245\u2013248 (2020).","journal-title":"Biosci. Biotechnol. Res. Commun."},{"key":"58509_CR21","doi-asserted-by":"crossref","unstructured":"Cutler, A., Cutler, D. R. & Stevens, J. R. Random forests. In Ensemble Machine Learning: Methods and Applications, 157\u2013175 (2012).","DOI":"10.1007\/978-1-4419-9326-7_5"},{"issue":"1","key":"58509_CR22","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D. & Wehenkel, L. Extremely randomized trees. Mach. Learn. 63 (1), 3\u201342 (2006).","journal-title":"Mach. Learn."},{"key":"58509_CR23","unstructured":"He, Z. et al. Gradient Boosting Machine: A Survey. arXiv preprint arXiv:1908.06951 (2019)."},{"key":"58509_CR24","unstructured":"Adamu, H. A., Muhammad, M. & Jingi, A. M. Application of gradient boosting algorithm in statistical modelling. Res. Rev. J. Stat. Math. Sci. 5, 11\u201318 (2019)."},{"key":"58509_CR25","unstructured":"Fafalios, S., Charonyktakis, P. & Tsamardinos, I. Gradient Boosting Trees. Gnosis Data Anal. PC, 1 (2020)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58509-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58509-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58509-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T09:45:44Z","timestamp":1787564744000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-58509-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,19]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["58509"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-58509-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,19]]},"assertion":[{"value":"11 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"26497"}}