Developing a Support Vector Machine Based QSPR Model to PredictGas-t<i>o</i>-Benzene Solvation Enthalpy of Organic Compounds

引用本文: 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 shu

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

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

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

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

English

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

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 Date: 13 December 2016
Revised Date: 16 January 2017
Available Online: 16 January 2017

Abstract: 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.

Key words:

Quantitative structure-property relationship
/ Gas-to-benzene solvation enthalpy
/ Descriptor;Enhanced replacement method
/ Support vector machine

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沈阳化工大学材料科学与工程学院沈阳 110142

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

English

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

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

English

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

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

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CCS Chemistry：工作高效不疲劳，只须让催化剂动起来

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CCS Chemistry：金黄色葡萄球菌醛基脱氢酶是如何识别特异性底物的？

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