Development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti-cancer drug within green chemistry solvent
Understanding the solubility of drug particles in solvents has remained a big challenge in different fields. Development of advanced computational methods to predict the solubility of drugs is an important necessity due to the difficulty and time-consuming characteristics of experimental measurement...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X23005798 |
| _version_ | 1797729880717131776 |
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| author | Muteb Alanazi Bader Huwaimel Jowaher Alanazi Tareq Nafea Alharby |
| author_facet | Muteb Alanazi Bader Huwaimel Jowaher Alanazi Tareq Nafea Alharby |
| author_sort | Muteb Alanazi |
| collection | DOAJ |
| description | Understanding the solubility of drug particles in solvents has remained a big challenge in different fields. Development of advanced computational methods to predict the solubility of drugs is an important necessity due to the difficulty and time-consuming characteristics of experimental measurements. This study used hybrid machine learning (ML) models utilizing two inputs, including Pressure (X1) and Temperature (X2), to investigate the current data, and correlate the solubility of drug particles in supercritical solvent. The methods of Random Forest (RF), Extra Trees (ET), and Gradient Boosting (GB) regression models were used to build the models on the available data. RF, ET, and GB have R-squared of 0.857, 0.998, and 0.992, based on the analysis results. Additionally, in terms of MAE, they illustrated error value 2.90E-06, 1.98E-06, and 1.10E-06, respectively. One more metric to consider is MAPE, in which the error rates for the three regions were 3.15E-01, 2.27E-01, and 1.16E-01, respectively. The DT method was chosen as the best method, and can be used to find optimal amounts, which is summarized as a vector: (×1 = 383, X2 = 333.15, Y = 6.004e-05). |
| first_indexed | 2024-03-12T11:36:04Z |
| format | Article |
| id | doaj.art-0349a43c05cd443ca2803082b2683de6 |
| institution | Directory Open Access Journal |
| issn | 2214-157X |
| language | English |
| last_indexed | 2024-03-12T11:36:04Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Thermal Engineering |
| spelling | doaj.art-0349a43c05cd443ca2803082b2683de62023-09-01T05:01:39ZengElsevierCase Studies in Thermal Engineering2214-157X2023-09-0149103273Development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti-cancer drug within green chemistry solventMuteb Alanazi0Bader Huwaimel1Jowaher Alanazi2Tareq Nafea Alharby3Department of Clinical Pharmacy, College of Pharmacy, University of Ha'il, Ha'il, 81442, Saudi Arabia; Corresponding author.Department of Pharmaceutical Chemistry, College of Pharmacy, University of Ha'il, Ha'il, 81442, Saudi Arabia; Medical and Diagnostic Research Center, University of Ha'il, Hail, 55473, Saudi ArabiaDepartment of Pharmacology and Toxicology, College of Pharmacy, University of Ha'il, Ha'il, 81442, Saudi ArabiaDepartment of Clinical Pharmacy, College of Pharmacy, University of Ha'il, Ha'il, 81442, Saudi ArabiaUnderstanding the solubility of drug particles in solvents has remained a big challenge in different fields. Development of advanced computational methods to predict the solubility of drugs is an important necessity due to the difficulty and time-consuming characteristics of experimental measurements. This study used hybrid machine learning (ML) models utilizing two inputs, including Pressure (X1) and Temperature (X2), to investigate the current data, and correlate the solubility of drug particles in supercritical solvent. The methods of Random Forest (RF), Extra Trees (ET), and Gradient Boosting (GB) regression models were used to build the models on the available data. RF, ET, and GB have R-squared of 0.857, 0.998, and 0.992, based on the analysis results. Additionally, in terms of MAE, they illustrated error value 2.90E-06, 1.98E-06, and 1.10E-06, respectively. One more metric to consider is MAPE, in which the error rates for the three regions were 3.15E-01, 2.27E-01, and 1.16E-01, respectively. The DT method was chosen as the best method, and can be used to find optimal amounts, which is summarized as a vector: (×1 = 383, X2 = 333.15, Y = 6.004e-05).http://www.sciencedirect.com/science/article/pii/S2214157X23005798Green processingSupercritical solventModelingDrug solubilityMachine learning |
| spellingShingle | Muteb Alanazi Bader Huwaimel Jowaher Alanazi Tareq Nafea Alharby Development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti-cancer drug within green chemistry solvent Case Studies in Thermal Engineering Green processing Supercritical solvent Modeling Drug solubility Machine learning |
| title | Development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti-cancer drug within green chemistry solvent |
| title_full | Development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti-cancer drug within green chemistry solvent |
| title_fullStr | Development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti-cancer drug within green chemistry solvent |
| title_full_unstemmed | Development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti-cancer drug within green chemistry solvent |
| title_short | Development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti-cancer drug within green chemistry solvent |
| title_sort | development of a novel machine learning approach to optimize important parameters for improving the solubility of an anti cancer drug within green chemistry solvent |
| topic | Green processing Supercritical solvent Modeling Drug solubility Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2214157X23005798 |
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