{"title":"Calculation of topological indices along with MATLAB coding in QSPR analysis of calcium channel-blocking cardiac drugs","authors":"Mehri Hasani, Masoud Ghods","doi":"10.1007/s10910-023-01570-9","DOIUrl":null,"url":null,"abstract":"<div><p>In this research, medications used for treating heart disease, specifically focusing on calcium channel blockers, were analyzed. A computer-based computing technique was used to simplify calculations and data analysis. Using MATLAB coding, we calculated their degree-based topological indices obtained from the M-polynomial. Various regression analyses were used to establish a relationship between these indices and the physicochemical features of the drugs. QSPR models were created to determine the effectiveness by correlating these indices with eight physicochemical features of the drugs. Confidence intervals were calculated at a 95% level for the intercept and slope in the linear regression models. The results indicate that the inverse sum indeg index (I) proved to be the most dependable indices for predicting boiling point, flashpoint, and enthalpy of vaporization. The symmetric division index (SDD) was effective in forecasting polarizability and molar refractivity, while the second modified Zagreb index (<span>\\(^{m}M_{2}\\)</span>) emerged as the best predictor for molar volume in linear, quadratic, and cubic regression models. Furthermore, the forgotten index (F) was identified as the top estimator for boiling point, flashpoint, enthalpy, and polar surface area in both quadratic and cubic regression models. Lastly, the SDD index, with a correlation coefficient of R = 1, is proposed as the most accurate estimator for the characteristics of polar surface area in quadratic, and cubic regression equations. Calculated feature values show a strong correlation with the actual values, indicating the indices’ reliable predictive capabilities.</p></div>","PeriodicalId":648,"journal":{"name":"Journal of Mathematical Chemistry","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mathematical Chemistry","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s10910-023-01570-9","RegionNum":3,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In this research, medications used for treating heart disease, specifically focusing on calcium channel blockers, were analyzed. A computer-based computing technique was used to simplify calculations and data analysis. Using MATLAB coding, we calculated their degree-based topological indices obtained from the M-polynomial. Various regression analyses were used to establish a relationship between these indices and the physicochemical features of the drugs. QSPR models were created to determine the effectiveness by correlating these indices with eight physicochemical features of the drugs. Confidence intervals were calculated at a 95% level for the intercept and slope in the linear regression models. The results indicate that the inverse sum indeg index (I) proved to be the most dependable indices for predicting boiling point, flashpoint, and enthalpy of vaporization. The symmetric division index (SDD) was effective in forecasting polarizability and molar refractivity, while the second modified Zagreb index (\(^{m}M_{2}\)) emerged as the best predictor for molar volume in linear, quadratic, and cubic regression models. Furthermore, the forgotten index (F) was identified as the top estimator for boiling point, flashpoint, enthalpy, and polar surface area in both quadratic and cubic regression models. Lastly, the SDD index, with a correlation coefficient of R = 1, is proposed as the most accurate estimator for the characteristics of polar surface area in quadratic, and cubic regression equations. Calculated feature values show a strong correlation with the actual values, indicating the indices’ reliable predictive capabilities.
期刊介绍：
The Journal of Mathematical Chemistry (JOMC) publishes original, chemically important mathematical results which use non-routine mathematical methodologies often unfamiliar to the usual audience of mainstream experimental and theoretical chemistry journals. Furthermore JOMC publishes papers on novel applications of more familiar mathematical techniques and analyses of chemical problems which indicate the need for new mathematical approaches.
Mathematical chemistry is a truly interdisciplinary subject, a field of rapidly growing importance. As chemistry becomes more and more amenable to mathematically rigorous study, it is likely that chemistry will also become an alert and demanding consumer of new mathematical results. The level of complexity of chemical problems is often very high, and modeling molecular behaviour and chemical reactions does require new mathematical approaches. Chemistry is witnessing an important shift in emphasis: simplistic models are no longer satisfactory, and more detailed mathematical understanding of complex chemical properties and phenomena are required. From theoretical chemistry and quantum chemistry to applied fields such as molecular modeling, drug design, molecular engineering, and the development of supramolecular structures, mathematical chemistry is an important discipline providing both explanations and predictions. JOMC has an important role in advancing chemistry to an era of detailed understanding of molecules and reactions.
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