Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation
Abstract This study investigates the application of various regression models for predicting drug solubility in polymer and API-polymer interactions in complex datasets. Four models—Gaussian Process Regression (GPR), Support Vector Regression (SVR), Bayesian Ridge Regression (BRR), and Kernel Ridge...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80952-8 |
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| author | Ahmad J. Obaidullah Wael A. Mahdi |
| author_facet | Ahmad J. Obaidullah Wael A. Mahdi |
| author_sort | Ahmad J. Obaidullah |
| collection | DOAJ |
| description | Abstract This study investigates the application of various regression models for predicting drug solubility in polymer and API-polymer interactions in complex datasets. Four models—Gaussian Process Regression (GPR), Support Vector Regression (SVR), Bayesian Ridge Regression (BRR), and Kernel Ridge Regression (KRR)—are evaluated. Preprocessing the dataset using the Z-score approach helped to detect outliers, further improving the accuracy and dependability of the analysis. Also, Fireworks Algorithm (FWA) is employed for hyper-parameter tuning in this work. The GPR model demonstrated superior performance, achieving the lowest MSE and MAE for both drug solubility and gamma predictions, with R2 scores of 0.9980 and 0.9950 for training and test data, respectively. The results of this study show the robustness of GPR in generating reliable and precise forecasts, thus providing a strong method for intricate regression tasks in pharmaceutical and other scientific fields. In addition, the Fireworks Algorithm (FWA) is presented as an optimization method, demonstrating its potential in improving the model’s predictive abilities by effectively exploring and exploiting the search space. The results emphasize the significance of choosing suitable regression models and optimization techniques to attain dependable and superior predictive analytics. |
| format | Article |
| id | doaj-art-9f19df72f7c44b12a788928f1773cbbf |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-9f19df72f7c44b12a788928f1773cbbf2024-12-01T12:21:52ZengNature PortfolioScientific Reports2045-23222024-11-0114111610.1038/s41598-024-80952-8Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validationAhmad J. Obaidullah0Wael A. Mahdi1Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud UniversityDepartment of Pharmaceutics, College of Pharmacy, King Saud UniversityAbstract This study investigates the application of various regression models for predicting drug solubility in polymer and API-polymer interactions in complex datasets. Four models—Gaussian Process Regression (GPR), Support Vector Regression (SVR), Bayesian Ridge Regression (BRR), and Kernel Ridge Regression (KRR)—are evaluated. Preprocessing the dataset using the Z-score approach helped to detect outliers, further improving the accuracy and dependability of the analysis. Also, Fireworks Algorithm (FWA) is employed for hyper-parameter tuning in this work. The GPR model demonstrated superior performance, achieving the lowest MSE and MAE for both drug solubility and gamma predictions, with R2 scores of 0.9980 and 0.9950 for training and test data, respectively. The results of this study show the robustness of GPR in generating reliable and precise forecasts, thus providing a strong method for intricate regression tasks in pharmaceutical and other scientific fields. In addition, the Fireworks Algorithm (FWA) is presented as an optimization method, demonstrating its potential in improving the model’s predictive abilities by effectively exploring and exploiting the search space. The results emphasize the significance of choosing suitable regression models and optimization techniques to attain dependable and superior predictive analytics.https://doi.org/10.1038/s41598-024-80952-8Drug designDrug solubilityGaussian process regressionHyper-parameter optimizationFireworks algorithm |
| spellingShingle | Ahmad J. Obaidullah Wael A. Mahdi Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation Scientific Reports Drug design Drug solubility Gaussian process regression Hyper-parameter optimization Fireworks algorithm |
| title | Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation |
| title_full | Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation |
| title_fullStr | Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation |
| title_full_unstemmed | Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation |
| title_short | Computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation |
| title_sort | computational intelligence analysis on drug solubility using thermodynamics and interaction mechanism via models comparison and validation |
| topic | Drug design Drug solubility Gaussian process regression Hyper-parameter optimization Fireworks algorithm |
| url | https://doi.org/10.1038/s41598-024-80952-8 |
| work_keys_str_mv | AT ahmadjobaidullah computationalintelligenceanalysisondrugsolubilityusingthermodynamicsandinteractionmechanismviamodelscomparisonandvalidation AT waelamahdi computationalintelligenceanalysisondrugsolubilityusingthermodynamicsandinteractionmechanismviamodelscomparisonandvalidation |