{"title":"Physicochemical significance of ChemDraw and Dragon computed parameters: correlation studies in the sets with aliphatic and aromatic substituents","authors":"","doi":"10.1007/s10910-023-01558-5","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>Quantitative Structure Activity Relationship (QSAR) requires the use of chemical descriptors which are either empirical or non-empirical. Although the ease of computation of computationally derived parameters such as given by ChemDraw software like CAA, CMA, CSEV and Dragon parameters like Au, Nc, Vs, TIC3, ATS2p etc. are easier to be used in the QSAR studies, but they still lack the biological interpretation as no prior knowledge of their physicochemical significance and their interrelationship is available. Therefore, the QSAR models developed using such parameters may be useful only in prediction of activity but are meaningless in terms of understanding the mode of action of the bioactive molecules. Thus, to fulfil this knowledge gap, and in continuation of our earlier work on physicochemical significance of topological parameters this study has been attempted to understand the empiricism of such computationally derived parameters in terms of their physicochemical significance. Here, we report that most of the ChemDraw and Dragon computed parameters are also best correlated with MR similar to topological parameters.</p>","PeriodicalId":648,"journal":{"name":"Journal of Mathematical Chemistry","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-01-18","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://doi.org/10.1007/s10910-023-01558-5","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
Quantitative Structure Activity Relationship (QSAR) requires the use of chemical descriptors which are either empirical or non-empirical. Although the ease of computation of computationally derived parameters such as given by ChemDraw software like CAA, CMA, CSEV and Dragon parameters like Au, Nc, Vs, TIC3, ATS2p etc. are easier to be used in the QSAR studies, but they still lack the biological interpretation as no prior knowledge of their physicochemical significance and their interrelationship is available. Therefore, the QSAR models developed using such parameters may be useful only in prediction of activity but are meaningless in terms of understanding the mode of action of the bioactive molecules. Thus, to fulfil this knowledge gap, and in continuation of our earlier work on physicochemical significance of topological parameters this study has been attempted to understand the empiricism of such computationally derived parameters in terms of their physicochemical significance. Here, we report that most of the ChemDraw and Dragon computed parameters are also best correlated with MR similar to topological parameters.
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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