Exploring QSPR in breast cancer drugs via entire neighborhood indices and regression models
Abstract Cancer is a life-threatening disease that can attack humans at any part of the body as a consequence of abnormal cell growth and proliferation, leading to tumors that can be fatal. Breast cancer is one of the deadliest ailments in the world after lung cancer. Through hormonal and genetic ch...
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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/s41598-025-12179-0 |
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| author | Alaa Altassan Anwar Saleh Hanaa Alashwali Marwa Hamed Najat Muthana |
| author_facet | Alaa Altassan Anwar Saleh Hanaa Alashwali Marwa Hamed Najat Muthana |
| author_sort | Alaa Altassan |
| collection | DOAJ |
| description | Abstract Cancer is a life-threatening disease that can attack humans at any part of the body as a consequence of abnormal cell growth and proliferation, leading to tumors that can be fatal. Breast cancer is one of the deadliest ailments in the world after lung cancer. Through hormonal and genetic changes that occur in DNA, breast cancer can affect women. The quantitative structural-property relationship (QSPR) is used to provide a comprehensive study of 16 drugs involved in the treatment of breast cancer. According to their chemical structure, the drugs being studied are modeled as molecular graphs. The purpose of this study is to examine the utility of new entire neighborhood topological indices in characterizing the physicochemical properties of a range of breast cancer drugs. Cubic regression analysis was initially employed, followed by multiple linear regression modeling to enhance the correlation between the entire neighborhood topological indices and some properties of the aforementioned drugs. The analysis results are presented and discussed, leading to conclusions about the potential of these new indices for pharmaceutical and chemical research on breast cancer treatments. |
| format | Article |
| id | doaj-art-cfbe8a56b6f04b3684173ca1e7582522 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-cfbe8a56b6f04b3684173ca1e75825222025-08-20T04:01:51ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-12179-0Exploring QSPR in breast cancer drugs via entire neighborhood indices and regression modelsAlaa Altassan0Anwar Saleh1Hanaa Alashwali2Marwa Hamed3Najat Muthana4Department of Mathematics, Faculty of Science, King Abdulaziz UniversityDepartment of Mathematics and Statistics, College of Science, University of JeddahDepartment of Mathematics, Faculty of Science, King Abdulaziz UniversityDepartment of Mathematics, Faculty of Science, King Abdulaziz UniversityDepartment of Mathematics and Statistics, College of Science, University of JeddahAbstract Cancer is a life-threatening disease that can attack humans at any part of the body as a consequence of abnormal cell growth and proliferation, leading to tumors that can be fatal. Breast cancer is one of the deadliest ailments in the world after lung cancer. Through hormonal and genetic changes that occur in DNA, breast cancer can affect women. The quantitative structural-property relationship (QSPR) is used to provide a comprehensive study of 16 drugs involved in the treatment of breast cancer. According to their chemical structure, the drugs being studied are modeled as molecular graphs. The purpose of this study is to examine the utility of new entire neighborhood topological indices in characterizing the physicochemical properties of a range of breast cancer drugs. Cubic regression analysis was initially employed, followed by multiple linear regression modeling to enhance the correlation between the entire neighborhood topological indices and some properties of the aforementioned drugs. The analysis results are presented and discussed, leading to conclusions about the potential of these new indices for pharmaceutical and chemical research on breast cancer treatments.https://doi.org/10.1038/s41598-025-12179-0Breast cancer drugsQSPR analysisEntire neighborhood topological indicesEntire topological indicesNeighborhood topological indices |
| spellingShingle | Alaa Altassan Anwar Saleh Hanaa Alashwali Marwa Hamed Najat Muthana Exploring QSPR in breast cancer drugs via entire neighborhood indices and regression models Scientific Reports Breast cancer drugs QSPR analysis Entire neighborhood topological indices Entire topological indices Neighborhood topological indices |
| title | Exploring QSPR in breast cancer drugs via entire neighborhood indices and regression models |
| title_full | Exploring QSPR in breast cancer drugs via entire neighborhood indices and regression models |
| title_fullStr | Exploring QSPR in breast cancer drugs via entire neighborhood indices and regression models |
| title_full_unstemmed | Exploring QSPR in breast cancer drugs via entire neighborhood indices and regression models |
| title_short | Exploring QSPR in breast cancer drugs via entire neighborhood indices and regression models |
| title_sort | exploring qspr in breast cancer drugs via entire neighborhood indices and regression models |
| topic | Breast cancer drugs QSPR analysis Entire neighborhood topological indices Entire topological indices Neighborhood topological indices |
| url | https://doi.org/10.1038/s41598-025-12179-0 |
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