A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors
Abstract In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for p...
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| Format: | Article |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85352-0 |
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| author | Huiling Qin Mudassar Rehman Muhammad Farhan Hanif Muhammad Yousaf Bhatti Muhammad Kamran Siddiqui Mohamed Abubakar Fiidow |
| author_facet | Huiling Qin Mudassar Rehman Muhammad Farhan Hanif Muhammad Yousaf Bhatti Muhammad Kamran Siddiqui Mohamed Abubakar Fiidow |
| author_sort | Huiling Qin |
| collection | DOAJ |
| description | Abstract In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy. To address this, we applied machine learning techniques, specifically linear regression models combined with K-fold cross-validation, to predict critical properties such as Density, Boiling Point, Flash Point, Bioconcentration Factor (BCF), Organic Carbon Partition Coefficient (KOC), Polarizability, and Molar Volume. The models were developed using data from ten anti-arrhythmic drugs ( $$D_1$$ to $$D_{10}$$ ). We evaluated the models based on performance metrics such as R and $$R^2$$ and obtained significant results. Most accurate predictions are obtained for polarizability from models with H(G) and $$ENT_{SS}(G)$$ . |
| format | Article |
| id | doaj-art-2dfc3f80b6f04c3eb75a8120c2f2a29e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2dfc3f80b6f04c3eb75a8120c2f2a29e2025-01-12T12:19:58ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-025-85352-0A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptorsHuiling Qin0Mudassar Rehman1Muhammad Farhan Hanif2Muhammad Yousaf Bhatti3Muhammad Kamran Siddiqui4Mohamed Abubakar Fiidow5Department of Rehabilitation Medicine, The Affiliated Hospital of Youjiang Medical University for NationalitiesDepartment of Mathematics, COMSATS University Islamabad, Lahore CampusDepartment of Mathematics and Statistics, The University of Lahore, Lahore CampusDepartment of Mathematics, COMSATS University Islamabad, Lahore CampusDepartment of Mathematics, COMSATS University Islamabad, Lahore CampusDepartment of Mathematical Sciences, Faculty of Science, Somali National University, Mogadishu CampusAbstract In recent years, machine learning has gained substantial attention for its ability to predict complex chemical and biological properties, including those of pharmaceutical compounds. This study proposes a machine learning-based quantitative structure-property relationship (QSPR) model for predicting the physicochemical properties of anti-arrhythmia drugs using topological descriptors. Anti-arrhythmic drug development is challenging due to the complex relationship between chemical structure and drug efficacy. To address this, we applied machine learning techniques, specifically linear regression models combined with K-fold cross-validation, to predict critical properties such as Density, Boiling Point, Flash Point, Bioconcentration Factor (BCF), Organic Carbon Partition Coefficient (KOC), Polarizability, and Molar Volume. The models were developed using data from ten anti-arrhythmic drugs ( $$D_1$$ to $$D_{10}$$ ). We evaluated the models based on performance metrics such as R and $$R^2$$ and obtained significant results. Most accurate predictions are obtained for polarizability from models with H(G) and $$ENT_{SS}(G)$$ .https://doi.org/10.1038/s41598-025-85352-0Anti-arrhythmia drugsLinear regression modelPyhton algorithmTopological indicesEntropyQSPR analysis |
| spellingShingle | Huiling Qin Mudassar Rehman Muhammad Farhan Hanif Muhammad Yousaf Bhatti Muhammad Kamran Siddiqui Mohamed Abubakar Fiidow A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors Scientific Reports Anti-arrhythmia drugs Linear regression model Pyhton algorithm Topological indices Entropy QSPR analysis |
| title | A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors |
| title_full | A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors |
| title_fullStr | A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors |
| title_full_unstemmed | A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors |
| title_short | A python approach for prediction of physicochemical properties of anti-arrhythmia drugs using topological descriptors |
| title_sort | python approach for prediction of physicochemical properties of anti arrhythmia drugs using topological descriptors |
| topic | Anti-arrhythmia drugs Linear regression model Pyhton algorithm Topological indices Entropy QSPR analysis |
| url | https://doi.org/10.1038/s41598-025-85352-0 |
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