Comparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysis
Abstract Quantitative structure-property relationship (QSPR) modeling has emerged as a pivotal tool in the field of medicinal chemistry and drug design, offering a predictive framework for understanding the correlation between chemical structure and physicochemical properties. Topological indices ar...
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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/s41598-025-88044-x |
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| author | Micheal Arockiaraj J. J. Jeni Godlin S. Radha Tariq Aziz Mitub Al-harbi |
| author_facet | Micheal Arockiaraj J. J. Jeni Godlin S. Radha Tariq Aziz Mitub Al-harbi |
| author_sort | Micheal Arockiaraj |
| collection | DOAJ |
| description | Abstract Quantitative structure-property relationship (QSPR) modeling has emerged as a pivotal tool in the field of medicinal chemistry and drug design, offering a predictive framework for understanding the correlation between chemical structure and physicochemical properties. Topological indices are mathematical descriptors derived from the molecular graphs that capture structural features and connectivity, playing a crucial role in QSPR analysis by quantitatively relating chemical structures to their physicochemical properties and biological activities. Lung cancer is characterized by its aggressive nature and late-stage diagnosis, often limiting treatment options and significantly impacting patient survival rates. This study focuses on the selection of drugs used to treat lung cancer, including dacomitinib, selpercatinib, tepotinib, trametinib, sotorasib, etoposide, alectinib, paclitaxel, dabrafenib, entrectinib, crizotinib, ceritinib, lorlatinib, afatinib, pralsetinib, brigatinib, erlotinib, adagrasib, gefitinib, vinorelbine, gemcitabine, docetaxel, and pemetrexed. Using molecular structural measures such as degree, neighborhood degree sum, and modified reverse degree, we have developed QSPR models to predict physicochemical properties through the topological indices derived from these structural measures. We then conducted a comparative analysis, incorporating correlation analysis, to identify the model with the highest predictive accuracy. |
| format | Article |
| id | doaj-art-fd9ab3d070aa414eb526b48110306e82 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
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| spelling | doaj-art-fd9ab3d070aa414eb526b48110306e822025-02-02T12:17:56ZengNature PortfolioScientific Reports2045-23222025-01-0115112010.1038/s41598-025-88044-xComparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysisMicheal Arockiaraj0J. J. Jeni Godlin1S. Radha2Tariq Aziz3Mitub Al-harbi4Department of Mathematics, Loyola CollegeSchool of Advanced Sciences, Vellore Institute of TechnologySchool of Advanced Sciences, Vellore Institute of TechnologyLaboratory of Animal Health Food Hygiene and Quality, University of IoanninaDepartment of Pharmacology and Toxicology, College of Pharmacy, King Saud UniversityAbstract Quantitative structure-property relationship (QSPR) modeling has emerged as a pivotal tool in the field of medicinal chemistry and drug design, offering a predictive framework for understanding the correlation between chemical structure and physicochemical properties. Topological indices are mathematical descriptors derived from the molecular graphs that capture structural features and connectivity, playing a crucial role in QSPR analysis by quantitatively relating chemical structures to their physicochemical properties and biological activities. Lung cancer is characterized by its aggressive nature and late-stage diagnosis, often limiting treatment options and significantly impacting patient survival rates. This study focuses on the selection of drugs used to treat lung cancer, including dacomitinib, selpercatinib, tepotinib, trametinib, sotorasib, etoposide, alectinib, paclitaxel, dabrafenib, entrectinib, crizotinib, ceritinib, lorlatinib, afatinib, pralsetinib, brigatinib, erlotinib, adagrasib, gefitinib, vinorelbine, gemcitabine, docetaxel, and pemetrexed. Using molecular structural measures such as degree, neighborhood degree sum, and modified reverse degree, we have developed QSPR models to predict physicochemical properties through the topological indices derived from these structural measures. We then conducted a comparative analysis, incorporating correlation analysis, to identify the model with the highest predictive accuracy.https://doi.org/10.1038/s41598-025-88044-xEdge partitionsTopological indicesQSPR modelsCancer drug structures |
| spellingShingle | Micheal Arockiaraj J. J. Jeni Godlin S. Radha Tariq Aziz Mitub Al-harbi Comparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysis Scientific Reports Edge partitions Topological indices QSPR models Cancer drug structures |
| title | Comparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysis |
| title_full | Comparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysis |
| title_fullStr | Comparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysis |
| title_full_unstemmed | Comparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysis |
| title_short | Comparative study of degree, neighborhood and reverse degree based indices for drugs used in lung cancer treatment through QSPR analysis |
| title_sort | comparative study of degree neighborhood and reverse degree based indices for drugs used in lung cancer treatment through qspr analysis |
| topic | Edge partitions Topological indices QSPR models Cancer drug structures |
| url | https://doi.org/10.1038/s41598-025-88044-x |
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