Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study
Abstract Machine learning is a vital tool in advancing drug development by accurately predicting the physical, chemical, and biological properties of various compounds. This study utilizes MATLAB program-based algorithms to calculate topological indices and machine learning algorithms to explore the...
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| Format: | Article |
| Language: | English |
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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/s41598-025-07022-5 |
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| author | Jalal Hatem Hussein Bayati Abid Mahboob Laiba Amin Muhammad Waheed Rasheed Abdu Alameri |
| author_facet | Jalal Hatem Hussein Bayati Abid Mahboob Laiba Amin Muhammad Waheed Rasheed Abdu Alameri |
| author_sort | Jalal Hatem Hussein Bayati |
| collection | DOAJ |
| description | Abstract Machine learning is a vital tool in advancing drug development by accurately predicting the physical, chemical, and biological properties of various compounds. This study utilizes MATLAB program-based algorithms to calculate topological indices and machine learning algorithms to explore their ability to predict the physio-chemical properties of asthma drugs. By combining machine learning with topological indices, we can conduct faster and more precise analyses of drug structures. As we deepen our understanding of the relationship between molecular structure and performance, the integration of machine learning with QSPR research highlights the significant potential of computational strategies in pharmaceutical discovery. The use of machine learning algorithms such as random forest and extreme gradient boosting is essential in this process. These algorithms leverage labeled data to predict complex molecular processes, aiding in the discovery of new medication options and enhancing their properties. These methods enhance the accuracy of physical and chemical property predictions, streamline the drug discovery process, and efficiently evaluate large datasets through machine learning. Ultimately, these advancements facilitate the development of innovative and effective treatments. |
| format | Article |
| id | doaj-art-da428b251f0345c68317dcb1ec35b8de |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-da428b251f0345c68317dcb1ec35b8de2025-08-24T11:31:14ZengNature PortfolioScientific Reports2045-23222025-08-0115112510.1038/s41598-025-07022-5Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR studyJalal Hatem Hussein Bayati0Abid Mahboob1Laiba Amin2Muhammad Waheed Rasheed3Abdu Alameri4Department of Mathematics, College of Science for Woman, University of BaghdadDepartment of Mathematics, Division of Science and Technology, University of EducationDepartment of Mathematics, COMSATS University Islamabad, Vehari CampusDepartment of Mathematics, COMSATS University Islamabad, Vehari CampusDepartment of Biomedical Engineering, University of Science and Technology, YemenAbstract Machine learning is a vital tool in advancing drug development by accurately predicting the physical, chemical, and biological properties of various compounds. This study utilizes MATLAB program-based algorithms to calculate topological indices and machine learning algorithms to explore their ability to predict the physio-chemical properties of asthma drugs. By combining machine learning with topological indices, we can conduct faster and more precise analyses of drug structures. As we deepen our understanding of the relationship between molecular structure and performance, the integration of machine learning with QSPR research highlights the significant potential of computational strategies in pharmaceutical discovery. The use of machine learning algorithms such as random forest and extreme gradient boosting is essential in this process. These algorithms leverage labeled data to predict complex molecular processes, aiding in the discovery of new medication options and enhancing their properties. These methods enhance the accuracy of physical and chemical property predictions, streamline the drug discovery process, and efficiently evaluate large datasets through machine learning. Ultimately, these advancements facilitate the development of innovative and effective treatments.https://doi.org/10.1038/s41598-025-07022-5Asthma drugsReducible topological descriptorsMATLAB and Python algorithmMachine learningRandom forestExtreme gradient boosting |
| spellingShingle | Jalal Hatem Hussein Bayati Abid Mahboob Laiba Amin Muhammad Waheed Rasheed Abdu Alameri Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study Scientific Reports Asthma drugs Reducible topological descriptors MATLAB and Python algorithm Machine learning Random forest Extreme gradient boosting |
| title | Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study |
| title_full | Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study |
| title_fullStr | Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study |
| title_full_unstemmed | Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study |
| title_short | Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study |
| title_sort | predictive modeling of asthma drug properties using machine learning and topological indices in a matlab based qspr study |
| topic | Asthma drugs Reducible topological descriptors MATLAB and Python algorithm Machine learning Random forest Extreme gradient boosting |
| url | https://doi.org/10.1038/s41598-025-07022-5 |
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