Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds

doi:10.3866/PKU.WHXB201701163

物理化学学报 >> 2017, Vol. 33 >> Issue (5): 918-926.doi: 10.3866/PKU.WHXB201701163

Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds

GOLMOHAMMADI Hassan^1,*(),DASHTBOZORGI Zahra²,KHOOSHECHIN Sajad²

¹ Young Researchers and Elite Club, Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran
² Young Researchers and Elite Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran

收稿日期:2016-12-13 发布日期:2017-04-20
通讯作者: GOLMOHAMMADI Hassan E-mail:hassan.gol@gmail.com

Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds

Hassan GOLMOHAMMADI^1,*(),Zahra DASHTBOZORGI²,Sajad KHOOSHECHIN²

¹ Young Researchers and Elite Club, Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch, Islamic Azad University, Tehran, Iran
² Young Researchers and Elite Club, Central Tehran Branch, Islamic Azad University, Tehran, Iran

Received:2016-12-13 Published:2017-04-20
Contact: Hassan GOLMOHAMMADI E-mail:hassan.gol@gmail.com

摘要/Abstract

摘要：

The purpose of this paper is to present a novel way to building quantitative structure-propertyrelationship (QSPR) models for predicting the gas-to-benzene solvation enthalpy (ΔH_Solv) of 158 organiccompounds based on molecular descriptors calculated from the structure alone. Different kinds of descriptorswere calculated for each compounds using dragon package. The variable selection technique of enhancedreplacement method (ERM) was employed to select optimal subset of descriptors. Our investigation revealsthat the dependence of physico-chemical properties on solvation enthalpy is a nonlinear observable fact andthat ERM method is unable to model the solvation enthalpy accurately. The standard error value of predictionset for support vector machine (SVM) is 1.681 kJ·mol^-1 while it is 4.624 kJ·mol^-1 for ERM. The resultsestablished that the calculated ΔH_Solv values by SVM were in good agreement with the experimental ones, andthe performances of the SVM models were superior to those obtained by ERM one. This indicates that SVMcan be used as an alternative modeling tool for QSPR studies.

关键词: Quantitative structure-property relationship, Gas-to-benzene solvation enthalpy, Descriptor, Enhanced replacement method, Support vector machine

Abstract:

Key words: Quantitative structure-property relationship, Gas-to-benzene solvation enthalpy, Descriptor, Enhanced replacement method, Support vector machine

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GOLMOHAMMADI Hassan,DASHTBOZORGI Zahra,KHOOSHECHIN Sajad. Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds[J]. 物理化学学报, 2017, 33(5), 918-926. doi: 10.3866/PKU.WHXB201701163

Hassan GOLMOHAMMADI,Zahra DASHTBOZORGI,Sajad KHOOSHECHIN. Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds[J]. Acta Phys. -Chim. Sin. 2017, 33(5), 918-926. doi: 10.3866/PKU.WHXB201701163

链接本文: https://www.whxb.pku.edu.cn/CN/10.3866/PKU.WHXB201701163

https://www.whxb.pku.edu.cn/CN/Y2017/V33/I5/918

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Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds

Developing a Support Vector Machine Based QSPR Model to PredictGas-to-Benzene Solvation Enthalpy of Organic Compounds

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