Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics
Abstract This work introduces a hybrid computational approach in which degree-based topological descriptors are harnessed with the aid of advanced regression models and artificial neural networks (ANNs) to predict the crucial physicochemical properties of 17 drugs for the treatment of bladder cancer...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-14175-w |
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| author | Huiling Qin Atef F. Hashem Muhammad Farhan Hanif Osman Abubakar Fiidow |
| author_facet | Huiling Qin Atef F. Hashem Muhammad Farhan Hanif Osman Abubakar Fiidow |
| author_sort | Huiling Qin |
| collection | DOAJ |
| description | Abstract This work introduces a hybrid computational approach in which degree-based topological descriptors are harnessed with the aid of advanced regression models and artificial neural networks (ANNs) to predict the crucial physicochemical properties of 17 drugs for the treatment of bladder cancer. Each molecule is assigned a molecular graph, from which a series of topological descriptors such as Zagreb indices, Randic index, Atom Bond Connectivity (ABC), and Symmetric Division Degree (SSD)are computed. These indices are used as input features by various regression models along with linear, cubic, and feedforward ANNs. The performance of the models is analyzed using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination $$(R^2)$$ . ANNs showed the best predictive performance with the $$R^2$$ value achieving 0.99. Moreover, SHAP (SHapley Additive exPlanations) analysis was used to explain the contribution of each descriptor toward the models’ predictions. The findings validate the promise of the combination of graph-theoretic descriptors with the tools of machine learning to achieve solid and interpretable models of molecular property prediction, which hold the potential for drug discovery and optimization in oncologic applications. |
| format | Article |
| id | doaj-art-af782e8a1eb146be9387242c9c3cd1d8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-af782e8a1eb146be9387242c9c3cd1d82025-08-20T03:42:48ZengNature PortfolioScientific Reports2045-23222025-07-0115113210.1038/s41598-025-14175-wGraph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeuticsHuiling Qin0Atef F. Hashem1Muhammad Farhan Hanif2Osman Abubakar Fiidow3Department of Rehabilitation, The Affiliated Hospital of Youjiang Medical University for NationalitiesDepartment of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)Department of Mathematics and Statistics, The University of LahoreDepartment of Public Health, Faculty of Health Science, Salaam UniversityAbstract This work introduces a hybrid computational approach in which degree-based topological descriptors are harnessed with the aid of advanced regression models and artificial neural networks (ANNs) to predict the crucial physicochemical properties of 17 drugs for the treatment of bladder cancer. Each molecule is assigned a molecular graph, from which a series of topological descriptors such as Zagreb indices, Randic index, Atom Bond Connectivity (ABC), and Symmetric Division Degree (SSD)are computed. These indices are used as input features by various regression models along with linear, cubic, and feedforward ANNs. The performance of the models is analyzed using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination $$(R^2)$$ . ANNs showed the best predictive performance with the $$R^2$$ value achieving 0.99. Moreover, SHAP (SHapley Additive exPlanations) analysis was used to explain the contribution of each descriptor toward the models’ predictions. The findings validate the promise of the combination of graph-theoretic descriptors with the tools of machine learning to achieve solid and interpretable models of molecular property prediction, which hold the potential for drug discovery and optimization in oncologic applications.https://doi.org/10.1038/s41598-025-14175-wArtificial Neural Networks (ANN)Topological IndicesDegree-Based DescriptorsCubic RegressionLinear RegressionQSPR |
| spellingShingle | Huiling Qin Atef F. Hashem Muhammad Farhan Hanif Osman Abubakar Fiidow Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics Scientific Reports Artificial Neural Networks (ANN) Topological Indices Degree-Based Descriptors Cubic Regression Linear Regression QSPR |
| title | Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics |
| title_full | Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics |
| title_fullStr | Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics |
| title_full_unstemmed | Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics |
| title_short | Graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics |
| title_sort | graph theoretic and machine learning approaches in molecular property prediction of bladder cancer therapeutics |
| topic | Artificial Neural Networks (ANN) Topological Indices Degree-Based Descriptors Cubic Regression Linear Regression QSPR |
| url | https://doi.org/10.1038/s41598-025-14175-w |
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