Computational machine learning estimation of digitoxin solubility in supercritical solvent at different temperatures utilizing ensemble methods
Abstract The solubility of medications in supercritical solvent is the most important factor that can be determined via appropriate computational tools. This work explores the modeling of digitoxin solubility as the case study in supercritical CO2 and solvent density utilizing ensemble methods. Temp...
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-15049-x |
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| author | Hadil Faris Alotaibi Waqed H. Hassan Ahmed Kateb Jumaah Al-Nussairi Narinderjit Singh Sawaran Singh Ahmed Salah Al-Shati M. M. Rekha Subhashree Ray Aashna Sinha Gunjan Garg |
| author_facet | Hadil Faris Alotaibi Waqed H. Hassan Ahmed Kateb Jumaah Al-Nussairi Narinderjit Singh Sawaran Singh Ahmed Salah Al-Shati M. M. Rekha Subhashree Ray Aashna Sinha Gunjan Garg |
| author_sort | Hadil Faris Alotaibi |
| collection | DOAJ |
| description | Abstract The solubility of medications in supercritical solvent is the most important factor that can be determined via appropriate computational tools. This work explores the modeling of digitoxin solubility as the case study in supercritical CO2 and solvent density utilizing ensemble methods. Temperature and pressure are the input parameters, while solvent density and digitoxin solubility are the output parameters. Several machine learning models along with optimizer were used for correlation of the dataset. Employing AdaBoost as an ensemble method, predictions from Bayesian Ridge Regression (BRR), Gaussian process regression (GPR), and K-nearest neighbors (KNN) are amalgamated. Sailfish Optimizer (SFO) is utilized for hyper-parameter tuning to enhance model performance. Results reveal that AdaBoost combined with ADA-GPR exhibits the lowest Average Absolute Relative Deviation (AARD%) values, with solubility achieving 7.74 and solvent density reaching 2.76, respectively. This underscores the efficacy of ensemble methods and hyper-parameter tuning in accurately predicting complex chemical properties in supercritical CO2 systems. |
| format | Article |
| id | doaj-art-0b1a68ce5a8440fa8b54ff3073d1bfc5 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-0b1a68ce5a8440fa8b54ff3073d1bfc52025-08-20T03:42:31ZengNature PortfolioScientific Reports2045-23222025-08-0115111110.1038/s41598-025-15049-xComputational machine learning estimation of digitoxin solubility in supercritical solvent at different temperatures utilizing ensemble methodsHadil Faris Alotaibi0Waqed H. Hassan1Ahmed Kateb Jumaah Al-Nussairi2Narinderjit Singh Sawaran Singh3Ahmed Salah Al-Shati4M. M. Rekha5Subhashree Ray6Aashna Sinha7Gunjan Garg8Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah Bint AbdulRahman UniversityUniversity of Warith Al-AnbiyaaAl-Manara College for Medical SciencesFaculty of Data Science and Information Technology, INTI International UniversityDepartment of Chemical Engineering and Petroleum Refining, Kut University CollegeDepartment of Chemistry and Biochemistry, School of Sciences, JAIN (Deemed to be University)Department of Biochemistry, IMS and SUM Hospital, Siksha ‘O’ Anusandhan (Deemed to be University)School of Applied and Life Sciences, Division of Research and Innovation, Uttaranchal UniversityCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara UniversityAbstract The solubility of medications in supercritical solvent is the most important factor that can be determined via appropriate computational tools. This work explores the modeling of digitoxin solubility as the case study in supercritical CO2 and solvent density utilizing ensemble methods. Temperature and pressure are the input parameters, while solvent density and digitoxin solubility are the output parameters. Several machine learning models along with optimizer were used for correlation of the dataset. Employing AdaBoost as an ensemble method, predictions from Bayesian Ridge Regression (BRR), Gaussian process regression (GPR), and K-nearest neighbors (KNN) are amalgamated. Sailfish Optimizer (SFO) is utilized for hyper-parameter tuning to enhance model performance. Results reveal that AdaBoost combined with ADA-GPR exhibits the lowest Average Absolute Relative Deviation (AARD%) values, with solubility achieving 7.74 and solvent density reaching 2.76, respectively. This underscores the efficacy of ensemble methods and hyper-parameter tuning in accurately predicting complex chemical properties in supercritical CO2 systems.https://doi.org/10.1038/s41598-025-15049-xPharmaceutical processDrug particlesMachine learningSolubilitySupercritical CO2 |
| spellingShingle | Hadil Faris Alotaibi Waqed H. Hassan Ahmed Kateb Jumaah Al-Nussairi Narinderjit Singh Sawaran Singh Ahmed Salah Al-Shati M. M. Rekha Subhashree Ray Aashna Sinha Gunjan Garg Computational machine learning estimation of digitoxin solubility in supercritical solvent at different temperatures utilizing ensemble methods Scientific Reports Pharmaceutical process Drug particles Machine learning Solubility Supercritical CO2 |
| title | Computational machine learning estimation of digitoxin solubility in supercritical solvent at different temperatures utilizing ensemble methods |
| title_full | Computational machine learning estimation of digitoxin solubility in supercritical solvent at different temperatures utilizing ensemble methods |
| title_fullStr | Computational machine learning estimation of digitoxin solubility in supercritical solvent at different temperatures utilizing ensemble methods |
| title_full_unstemmed | Computational machine learning estimation of digitoxin solubility in supercritical solvent at different temperatures utilizing ensemble methods |
| title_short | Computational machine learning estimation of digitoxin solubility in supercritical solvent at different temperatures utilizing ensemble methods |
| title_sort | computational machine learning estimation of digitoxin solubility in supercritical solvent at different temperatures utilizing ensemble methods |
| topic | Pharmaceutical process Drug particles Machine learning Solubility Supercritical CO2 |
| url | https://doi.org/10.1038/s41598-025-15049-x |
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