https://m2.mtmt.hu/ hun 2026-10-25 15:53 JournalArticle 35801953 APPROVED true 0 false 2025-03-03T13:06:13.013+0000 2025-03-03T13:05:14.806+0000 2025-03-03T13:04:54.084+0000 true 10098688 Lantos Jonatán /api/admin/10098688 Admin Lantos Jonatán (Egis admin5) true 2025-03-03T13:05:15.054+0000 2025-03-03T13:05:15.054+0000 false false true 24 24 /api/publicationtype/24 PublicationType Folyóiratcikk 1 true 24 JournalArticle true 10000059 Article true 24 24 /api/publicationtype/24 PublicationType Folyóiratcikk 1 true 24 JournalArticle /api/subtype/10000059 Szakcikk SubType Szakcikk (Folyóiratcikk) 101 true 10000059 true 1 /api/category/1 Category Tudományos true 1 Alqarni, Mohammed true true /api/journal/10019966 REVIEWED SCIENTIFIC REPORTS 2045-2322 true false 10019966 false 10019966 true 2045-2322 Journal FOREIGN 15 1 2241 2241 2025 true 2025 true true true false false false GOLD 2025-03-03 false http://www.nature.com/srep/index.html 0 0 0 0 0 0 0 0 0 0 0 0 1 1 Q1 A nemzetközi false false false false 2025-06-01T12:05:14.717+0000 7 13 0 2 0 true Language 10002 /api/language/10002 Angol Angol English true 2 true PersonAuthorship 123594673 /api/authorship/123594673 Alqarni, Mohammed 1 0.5 true false false Alqarni Mohammed true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PersonAuthorship 123594674 /api/authorship/123594674 Alqarni, Ali 2 0.5 false true false Alqarni Ali true false false AuthorshipType 1 /api/authorshiptype/1 Szerző 0 true 0 true false true PublicationIdentifier 28571515 /api/publicationidentifier/28571515 DOI: 10.1038/s41598-024-84741-1 PlainSource 6 /api/publicationsource/6 DOI PublicationSourceType 10001 /api/publicationsourcetype/10001 DOI true true true DOI DOI https://doi.org/@@@ true true 6 true IDENTICAL 10.1038/s41598-024-84741-1 https://doi.org/10.1038/s41598-024-84741-1 false true PublicationIdentifier 28571517 /api/publicationidentifier/28571517 WoS: 001399010800001 PlainSource 1 /api/publicationsource/1 WoS PublicationSourceType 10003 /api/publicationsourcetype/10003 Indexelő adatbázis false true true WoS WoS https://www.webofscience.com/wos/woscc/full-record/@@@ true true 1 true 001399010800001 https://www.webofscience.com/wos/woscc/full-record/001399010800001 false true PublicationIdentifier 28571516 /api/publicationidentifier/28571516 Egyéb URL: https://www.nature.com/articles/s41598-024-84741-1 PlainSource 40 /api/publicationsource/40 Egyéb URL PublicationSourceType 10006 /api/publicationsourcetype/10006 Link true true true Egyéb URL Other URL @@@ true true 40 true https://www.nature.com/articles/s41598-024-84741-1 https://www.nature.com/articles/s41598-024-84741-1 false true MtaRating 519359 /api/mtarating/519359 IX. Szociológiai Tudományos Bizottság:A nemzetközi SCIENTIFIC REPORTS 2045-2322 2045-2322 RatingType 10007 /api/ratingtype/10007 IX. Szociológiai Tudományos Bizottság SZTB Institute 14102 /api/institute/14102 Szociológiai Tudományos Bizottság IXGJO SZTB [1901-] 0 false true Szociológiai Tudományos Bizottság Szociológiai Tudományos Bizottság true 14102 true true true A nemzetközi true 19359 true MtaRating 11551161 /api/mtarating/11551161 IX. Regionális Tudományok Bizottsága:B nemzetközi SCIENTIFIC REPORTS 2045-2322 2045-2322 RatingType 10010 /api/ratingtype/10010 IX. Regionális Tudományok Bizottsága RegTB Institute 14100 /api/institute/14100 Regionális Tudományok Bizottsága IXGJO RTB [1901-] 0 false true Regionális Tudományok Bizottsága Regionális Tudományok Bizottsága true 14100 true true true B nemzetközi false true SjrRating 11606390 /api/sjrrating/11606390 sjr:D1 (2025) Scopus - Multidisciplinary SCIENTIFIC REPORTS 2045-2322 2045-2322 15 0.1 journal RatingType 10002 /api/ratingtype/10002 sjr sjr true true ClassificationExternal 1000 /api/classificationexternal/1000 Scopus - Multidisciplinary true 1000 true D1 FROM_PREVIOUS_TO_LAST_YEAR true true Reference 64459699 /api/reference/64459699 1. Pu, S. & Hadinoto, K. Yield enhancement and phase behaviors of cyclic peptide drug crystallization in the presence of polyethylene glycol additive. Chem. Eng. Res. Des. 205, 354–363 (2024)., DOI: 10.1016/j.cherd.2024.04.010 1 10.1016/j.cherd.2024.04.010 false true Reference 64459700 /api/reference/64459700 2. Wang, H. et al. Crystallization and intermolecular hydrogen bonding in carbamazepine-polyvinyl pyrrolidone solid dispersions: An experiment and molecular simulation study on drug content variation. Int. J. Pharm. 666, 124769 (2024)., DOI: 10.1016/j.ijpharm.2024.124769 2 10.1016/j.ijpharm.2024.124769 false true Reference 64459701 /api/reference/64459701 3. Soto, R. et al. Ketoprofen solubility in pure organic solvents using in situ FTIR and UV–vis and analysis of solution thermodynamics. Org. Process Res. Dev. 25(11), 2403–2414 (2021)., DOI: 10.1021/acs.oprd.1c00156 3 10.1021/acs.oprd.1c00156 false true Reference 64459702 /api/reference/64459702 4. Zeng, L., Rasmuson, Å. C. & Svärd, M. Solubility of two polymorphs of tolbutamide in n-propanol: Comparison of methods. J. Pharm. Sci. 109(10), 3021–3026 (2020)., DOI: 10.1016/j.xphs.2020.06.022 4 10.1016/j.xphs.2020.06.022 false true Reference 64459703 /api/reference/64459703 5. Quilló, G. L. et al. In Dynamic Optimization of Active Pharmaceutical Ingredient (Semi-)Batch Crystallization using Population Balance Modelling, in Computer Aided Chemical Engineering (eds Kokossis, A. C. et al.) 1495–1500 (Elsevier, 2023). 5 false true Reference 64459704 /api/reference/64459704 6. Trampuž, M., Teslić, D. & Likozar, B. Crystal-size distribution-based dynamic process modelling, optimization, and scaling for seeded batch cooling crystallization of active pharmaceutical ingredients (API). Chem. Eng. Res. Des. 165, 254–269 (2021)., DOI: 10.1016/j.cherd.2020.10.029 6 10.1016/j.cherd.2020.10.029 false true Reference 64459705 /api/reference/64459705 7. Orosz, Á. et al. Dynamic modeling and optimal design space determination of pharmaceutical crystallization processes: realizing the synergy between off-the-shelf laboratory and industrial scale data. Ind. Eng. Chem. Res. 63(9), 4068–4082 (2024)., DOI: 10.1021/acs.iecr.3c03954 7 10.1021/acs.iecr.3c03954 false true Reference 64459706 /api/reference/64459706 8. Sodeifian, G. et al. Determination of Regorafenib monohydrate (colorectal anticancer drug) solubility in supercritical CO2: Experimental and thermodynamic modeling. Heliyon 10(8), e29049 (2024)., DOI: 10.1016/j.heliyon.2024.e29049 8 10.1016/j.heliyon.2024.e29049 false true Reference 64459707 /api/reference/64459707 9. Sodeifian, G. et al. Solubility measurement of Ceftriaxone sodium in SC-CO2 and thermodynamic modeling using PR-KM EoS and vdW mixing rules with semi-empirical models. Case Stud. Therm. Eng. 61, 105074 (2024)., DOI: 10.1016/j.csite.2024.105074 9 10.1016/j.csite.2024.105074 false true Reference 64459708 /api/reference/64459708 10. 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)., DOI: 10.1016/j.chemolab.2024.105241 10 10.1016/j.chemolab.2024.105241 false true Reference 64459709 /api/reference/64459709 11. Yu, Y., Sun, C. & Jiang, W. A comprehensive study of pharmaceutics solubility in supercritical solvent through diverse thermodynamic and hybrid Machine learning approaches. Int. J. Pharm. 664, 124579 (2024)., DOI: 10.1016/j.ijpharm.2024.124579 11 10.1016/j.ijpharm.2024.124579 false true Reference 64459710 /api/reference/64459710 12. Dutta S et al. Robust counterfactual explanations for tree-based ensembles. In International Conference on Machine Learning. PMLR. (2022). 12 false true Reference 64459711 /api/reference/64459711 13. Jeong, J.-S. et al. Measurement and correlation of solubility of rivaroxaban in dichloromethane and primary alcohol binary solvent mixtures at different temperatures. J. Mol. Liq. 357, 119064 (2022)., DOI: 10.1016/j.molliq.2022.119064 13 10.1016/j.molliq.2022.119064 false true Reference 64459712 /api/reference/64459712 14. Alqarni, M., Alqarni, A. Computational intelligence investigations on the correlation of pharmaceutical solubility in mixtures of binary solvents: Effect of composition and temperature. Chinese Journal of Physics 93, 503–514, https://doi.org/10.1016/j.cjph.2024.12.021 (2025)., DOI: 10.1016/j.cjph.2024.12.021 14 10.1016/j.cjph.2024.12.021 false true Reference 64459713 /api/reference/64459713 15. Seger C. An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing. Student thesis, KTH Royal Institute of Technology, (2018). 15 false true Reference 64459714 /api/reference/64459714 16. Patro, S., Normalization: A preprocessing stage. arXiv preprint, 1503.06462: arXiv:1503.06462. (2015)., DOI: 10.17148/IARJSET.2015.2305 16 10.17148/IARJSET.2015.2305 false true Reference 64459715 /api/reference/64459715 17. Oyeyemi, G. M., Bukoye, A. & Akeyede, I. Comparison of outlier detection procedures in multiple linear regressions. Am. J. Math. Stat. 5(1), 37–41 (2015). 17 false true Reference 64459716 /api/reference/64459716 18. Otchere, D. A. et al. Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions. J. Pet. Sci. Eng. 208, 109244 (2022)., DOI: 10.1016/j.petrol.2021.109244 18 10.1016/j.petrol.2021.109244 false true Reference 64459717 /api/reference/64459717 19. Rao, H. et al. Feature selection based on artificial bee colony and gradient boosting decision tree. Appl. Soft Comput. 74, 634–642 (2019)., DOI: 10.1016/j.asoc.2018.10.036 19 10.1016/j.asoc.2018.10.036 false true Reference 64459718 /api/reference/64459718 20. Sun, P., Huo, S., He, T. Multiple machine learning models in estimating viscosity of crude oil: Comparisons and optimization for reservoir simulation. Journal of Molecular Liquids 384, 122251. https://doi.org/10.1016/j.molliq.2023.122251 (2023)., DOI: 10.1016/j.molliq.2023.122251 20 10.1016/j.molliq.2023.122251 false true Reference 64459719 /api/reference/64459719 21. Pedregosa, F. et al. Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011). 21 false true Reference 64459720 /api/reference/64459720 22. Ke G, et al. Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30. (2017). 22 false true Reference 64459721 /api/reference/64459721 23. Pavlov, Y. L. Random forests. In Random Forests (ed. Kani, B.) 1–2 (De Gruyter, 2019). 23 false true Reference 64459722 /api/reference/64459722 24. Heddam, S. Extremely randomized trees versus random forest, group method of data handling, and artificial neural network. In Handbook of Hydroinformatics 291–304 (Elsevier, 2023)., DOI: 10.1016/B978-0-12-821961-4.00006-3 24 10.1016/B978-0-12-821961-4.00006-3 false true Reference 64459723 /api/reference/64459723 25. Geurts, P., Ernst, D. & Wehenkel, L. Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)., DOI: 10.1007/s10994-006-6226-1 25 10.1007/s10994-006-6226-1 false true Reference 64459724 /api/reference/64459724 26. Kocev D, M Ceci. Ensembles of extremely randomized trees for multi-target regression. In Discovery Science: 18th International Conference, DS 2015, Banff, AB, Canada, October 4–6, 2015. Proceedings 18. Springer. (2015). 26 false true Reference 64459725 /api/reference/64459725 27. Majrashi, M.A.A. et al. Nonsteroidal anti-inflammatory drug solubility optimization through green chemistry solvent: Artificial intelligence technique. Case Studies in Thermal Engineering 53, 103767 https://doi.org/10.1016/j.csite.2023.103767 (2024)., DOI: 10.1016/j.csite.2023.103767 27 10.1016/j.csite.2023.103767 false true Reference 64459726 /api/reference/64459726 28. Chou, J.-S. & Truong, D.-N. A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Appl. Math. Comput. 389, 125535 (2021). 28 false true Reference 64459727 /api/reference/64459727 29. Xia, S., Wang, Y. Preparation of solid-dosage nanomedicine via green chemistry route: Advanced computational simulation of nanodrug solubility prediction using machine learning models, Journal of Molecular Liquids 375, 121319. https://doi.org/10.1016/j.molliq.2023.121319 (2023)., DOI: 10.1016/j.molliq.2023.121319 29 10.1016/j.molliq.2023.121319 false true Reference 64459728 /api/reference/64459728 30. Olabi, A. et al. Boosting carbon dioxide adsorption capacity applying Jellyfish optimization and ANFIS-based modelling. Ain Shams Eng. J. 14, 101931 (2022)., DOI: 10.1016/j.asej.2022.101931 30 10.1016/j.asej.2022.101931 false true /api/publication/35801953 Alqarni Mohammed et al. Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization. (2025) SCIENTIFIC REPORTS 2045-2322 15 1<div class="JournalArticle Publication long-list"> <div class="authors"> <img title="Idézőközlemény" style="float: left" src="/frontend/resources/grid/publication-citation-icon.png"> <div class="autype autype0"> <span class="author-name" >Alqarni Mohammed </span> ;&nbsp;&nbsp;&nbsp; <span class="author-name" >Alqarni Ali </span> </div> </div> <div class="title"><a href="/gui2/?mode=browse&params=publication;35801953" target="_blank">Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization</a></div> <div> <span class="journal-title">SCIENTIFIC REPORTS</span> <span class="journal-issn">( <a target="_blank" href="https://portal.issn.org/resource/ISSN/2045-2322">2045-2322</a>)</span>: <span class="journal-volume">15</span> <span class="journal-issue">1</span> <span class="page"> Paper 2241. </span> <span class="year">(2025)</span> </div> <div class="pub-footer"> <span class="language" xmlns="http://www.w3.org/1999/html">Nyelv: Angol | </span> <span class="identifiers"> <span class="id identifier oa_none" title="none"> <a style="color:blue" title="10.1038/s41598-024-84741-1" target="_blank" href="https://doi.org/10.1038/s41598-024-84741-1"> DOI </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:black" title="001399010800001" target="_blank" href="https://www.webofscience.com/wos/woscc/full-record/001399010800001"> WoS </a> </span> <span class="id identifier oa_none" title="none"> <a style="color:black" title="https://www.nature.com/articles/s41598-024-84741-1" target="_blank" href="https://www.nature.com/articles/s41598-024-84741-1"> Egyéb URL </a> </span> </span> <div class="publication-citation"> <a target="_blank" href="/api/publication?cond=citations.related;eq;35801953&sort=publishedYear,desc&sort=title"> Idézett közlemények száma: 1 </a> </div> <div class="mtid"><span class="long-pub-mtid">Közlemény: 35801953</span> | <span class="status-data status-APPROVED"> Nyilvános </span> Idéző | <span class="type-subtype">Folyóiratcikk ( Szakcikk ) </span> | <span class="pub-category">Tudományos</span> | <span class="publication-sourceOfData">kézi felvitel</span> </div> <div class="lastModified">Utolsó módosítás: 2025.03.03. 14:05 Lantos Jonatán (Egis admin5) </div> </div></div>