Role of topological indices in predictive modeling and ranking of drugs treating eye disorders
Abstract Topological indices (TIs) of chemical graphs of drugs hold the potential to compute important properties and biological activities leading to more thoughtful drug design. Here, we considered certain drugs treating eye-related disorders, including cataract, glaucoma, diabetic retinopathy, an...
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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/s41598-024-81482-z |
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| author | Nazeran Idrees Esha Noor Saima Rashid Fekadu Tesgera Agama |
| author_facet | Nazeran Idrees Esha Noor Saima Rashid Fekadu Tesgera Agama |
| author_sort | Nazeran Idrees |
| collection | DOAJ |
| description | Abstract Topological indices (TIs) of chemical graphs of drugs hold the potential to compute important properties and biological activities leading to more thoughtful drug design. Here, we considered certain drugs treating eye-related disorders, including cataract, glaucoma, diabetic retinopathy, and macular degeneration. By combining modeling and decision-makings approaches, this study presents a cost-effective way to comprehend the behavior of molecules. First, the topological indices of chemical graphs of molecules are determined, which provides valuable insights into their behavior. These models are first trained using known data and are also validated by the dataset of known properties. Models for quantitative structure property relations (QSPR) are computed using the quadratic regression method. TIs having correlation value greater than 0.7 with properties like molar weight, index of refraction, molar volume, polarizability, and molar refraction are taken in this work. Weights are assigned to different properties of drugs depending upon the correlation of the properties with topological indices. Furthermore, we used the multiple-choice decision-making techniques TOPSIS and SAW, to rank the drugs treating eye disorders to create well-informed selections. We can precisely forecast the behavior of chemicals by utilizing machine learning to analyze large amounts of data. This method may contribute to the discovery of new relevant drugs with desirable properties and helpful in comprehending the effects of chemicals on their efficacy. |
| format | Article |
| id | doaj-art-b731a6b6ef27449babad656b65418f8a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-b731a6b6ef27449babad656b65418f8a2025-01-12T12:17:20ZengNature PortfolioScientific Reports2045-23222025-01-0115111410.1038/s41598-024-81482-zRole of topological indices in predictive modeling and ranking of drugs treating eye disordersNazeran Idrees0Esha Noor1Saima Rashid2Fekadu Tesgera Agama3Department of Mathematics, Government College University FaisalabadDepartment of Mathematics, Government College University FaisalabadDepartment of Mathematics, Government College University FaisalabadDepartment of Mathematics, Wollega UniversityAbstract Topological indices (TIs) of chemical graphs of drugs hold the potential to compute important properties and biological activities leading to more thoughtful drug design. Here, we considered certain drugs treating eye-related disorders, including cataract, glaucoma, diabetic retinopathy, and macular degeneration. By combining modeling and decision-makings approaches, this study presents a cost-effective way to comprehend the behavior of molecules. First, the topological indices of chemical graphs of molecules are determined, which provides valuable insights into their behavior. These models are first trained using known data and are also validated by the dataset of known properties. Models for quantitative structure property relations (QSPR) are computed using the quadratic regression method. TIs having correlation value greater than 0.7 with properties like molar weight, index of refraction, molar volume, polarizability, and molar refraction are taken in this work. Weights are assigned to different properties of drugs depending upon the correlation of the properties with topological indices. Furthermore, we used the multiple-choice decision-making techniques TOPSIS and SAW, to rank the drugs treating eye disorders to create well-informed selections. We can precisely forecast the behavior of chemicals by utilizing machine learning to analyze large amounts of data. This method may contribute to the discovery of new relevant drugs with desirable properties and helpful in comprehending the effects of chemicals on their efficacy.https://doi.org/10.1038/s41598-024-81482-zTopological indexChemical graphTOPSISPhysiochemical properties |
| spellingShingle | Nazeran Idrees Esha Noor Saima Rashid Fekadu Tesgera Agama Role of topological indices in predictive modeling and ranking of drugs treating eye disorders Scientific Reports Topological index Chemical graph TOPSIS Physiochemical properties |
| title | Role of topological indices in predictive modeling and ranking of drugs treating eye disorders |
| title_full | Role of topological indices in predictive modeling and ranking of drugs treating eye disorders |
| title_fullStr | Role of topological indices in predictive modeling and ranking of drugs treating eye disorders |
| title_full_unstemmed | Role of topological indices in predictive modeling and ranking of drugs treating eye disorders |
| title_short | Role of topological indices in predictive modeling and ranking of drugs treating eye disorders |
| title_sort | role of topological indices in predictive modeling and ranking of drugs treating eye disorders |
| topic | Topological index Chemical graph TOPSIS Physiochemical properties |
| url | https://doi.org/10.1038/s41598-024-81482-z |
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