{"title":"利用基于 CDFT 和信息论的描述符建立正辛醇/水分配系数和汽化焓的 QSPR 模型","authors":"Arpita Poddar, Akshay Chordia, Pratim Kumar Chattaraj","doi":"10.1007/s12039-024-02250-0","DOIUrl":null,"url":null,"abstract":"<div><p>The quantitative structure-property relationship (QSPR) technique is used to gauge the n-octanol/water partition coefficient (log <i>K</i><sub>OW</sub>) and enthalpy of vaporization (<i>∆</i><sub>vap</sub><i>H</i><sub>m</sub>) of 133 Polychlorinated Biphenyls (PCBs) using conceptual density functional theory (CDFT)-based global reactivity and information-theory (IT) based parameters. Regression models are established using linear and multi-linear relationships to correlate the observed physicochemical properties of PCBs with the predicted ones. The study explored the significance of CDFT and IT descriptors, and based on the calculation of Pearson correlation coefficient values, the selection of suitable descriptors is made for successful QSPR models of selected PCBs. It is found that some of the CDFT parameters are highly correlated with the IT parameters, as suggested by their high Pearson correlation coefficient values for PCB systems. The regression model generated using the descriptors <i>I</i><sub><i>G</i></sub>, <i>g</i><sub>1</sub>, <i>g</i><sub>2</sub>, <i>EA</i>, <i>η</i> for predicting log <i>K</i><sub>OW</sub> and <i>I</i><sub><i>F</i></sub><i>, g</i><sub>3</sub>, <i>η</i>, <i>S</i><sub><i>S</i></sub>, <i>S</i><sub><i>GBP</i></sub> for predicting <i>∆</i><sub>vap</sub><i>H</i><sub>m</sub> gives <i>R</i><sup>2</sup> value of 0.9342 and 0.8662, respectively, for the selected 133 PCB congeners. Furthermore, to verify the descriptor selection, a machine learning approach is also used to develop QSPR models in this study.</p><h3>Graphical Abstract</h3><p>QSPR modelling using CDFT and information theory-based descriptors for predicting n-octanol/water partition coefficient and enthalpy of vaporization for the selected PCBs\n</p><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":616,"journal":{"name":"Journal of Chemical Sciences","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"QSPR models for n-octanol/water partition coefficient and enthalpy of vaporization using CDFT and information theory-based descriptors\",\"authors\":\"Arpita Poddar, Akshay Chordia, Pratim Kumar Chattaraj\",\"doi\":\"10.1007/s12039-024-02250-0\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The quantitative structure-property relationship (QSPR) technique is used to gauge the n-octanol/water partition coefficient (log <i>K</i><sub>OW</sub>) and enthalpy of vaporization (<i>∆</i><sub>vap</sub><i>H</i><sub>m</sub>) of 133 Polychlorinated Biphenyls (PCBs) using conceptual density functional theory (CDFT)-based global reactivity and information-theory (IT) based parameters. Regression models are established using linear and multi-linear relationships to correlate the observed physicochemical properties of PCBs with the predicted ones. The study explored the significance of CDFT and IT descriptors, and based on the calculation of Pearson correlation coefficient values, the selection of suitable descriptors is made for successful QSPR models of selected PCBs. It is found that some of the CDFT parameters are highly correlated with the IT parameters, as suggested by their high Pearson correlation coefficient values for PCB systems. The regression model generated using the descriptors <i>I</i><sub><i>G</i></sub>, <i>g</i><sub>1</sub>, <i>g</i><sub>2</sub>, <i>EA</i>, <i>η</i> for predicting log <i>K</i><sub>OW</sub> and <i>I</i><sub><i>F</i></sub><i>, g</i><sub>3</sub>, <i>η</i>, <i>S</i><sub><i>S</i></sub>, <i>S</i><sub><i>GBP</i></sub> for predicting <i>∆</i><sub>vap</sub><i>H</i><sub>m</sub> gives <i>R</i><sup>2</sup> value of 0.9342 and 0.8662, respectively, for the selected 133 PCB congeners. Furthermore, to verify the descriptor selection, a machine learning approach is also used to develop QSPR models in this study.</p><h3>Graphical Abstract</h3><p>QSPR modelling using CDFT and information theory-based descriptors for predicting n-octanol/water partition coefficient and enthalpy of vaporization for the selected PCBs\\n</p><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>\",\"PeriodicalId\":616,\"journal\":{\"name\":\"Journal of Chemical Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Chemical Sciences\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s12039-024-02250-0\",\"RegionNum\":4,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Chemical Sciences","FirstCategoryId":"92","ListUrlMain":"https://link.springer.com/article/10.1007/s12039-024-02250-0","RegionNum":4,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
QSPR models for n-octanol/water partition coefficient and enthalpy of vaporization using CDFT and information theory-based descriptors
The quantitative structure-property relationship (QSPR) technique is used to gauge the n-octanol/water partition coefficient (log KOW) and enthalpy of vaporization (∆vapHm) of 133 Polychlorinated Biphenyls (PCBs) using conceptual density functional theory (CDFT)-based global reactivity and information-theory (IT) based parameters. Regression models are established using linear and multi-linear relationships to correlate the observed physicochemical properties of PCBs with the predicted ones. The study explored the significance of CDFT and IT descriptors, and based on the calculation of Pearson correlation coefficient values, the selection of suitable descriptors is made for successful QSPR models of selected PCBs. It is found that some of the CDFT parameters are highly correlated with the IT parameters, as suggested by their high Pearson correlation coefficient values for PCB systems. The regression model generated using the descriptors IG, g1, g2, EA, η for predicting log KOW and IF, g3, η, SS, SGBP for predicting ∆vapHm gives R2 value of 0.9342 and 0.8662, respectively, for the selected 133 PCB congeners. Furthermore, to verify the descriptor selection, a machine learning approach is also used to develop QSPR models in this study.
Graphical Abstract
QSPR modelling using CDFT and information theory-based descriptors for predicting n-octanol/water partition coefficient and enthalpy of vaporization for the selected PCBs
期刊介绍：
Journal of Chemical Sciences is a monthly journal published by the Indian Academy of Sciences. It formed part of the original Proceedings of the Indian Academy of Sciences – Part A, started by the Nobel Laureate Prof C V Raman in 1934, that was split in 1978 into three separate journals. It was renamed as Journal of Chemical Sciences in 2004. The journal publishes original research articles and rapid communications, covering all areas of chemical sciences. A significant feature of the journal is its special issues, brought out from time to time, devoted to conference symposia/proceedings in frontier areas of the subject, held not only in India but also in other countries.
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