A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters.

Li Wen,Yong-Ming Cai,Qiao Cai,Wei Li,Qing Li

doi:10.1155/2017/4914272

Abstract

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEPext), fitting correlation coefficient (R2), and leave-one-out cross-validation (Q2LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R2 and Q2LOO of 4 kinds of nonlinear models are more than 0.6 and SDEPext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R2 = 0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.

Highlights

Qiu et al [24] optimized the molecular structures of eleven kinds of phydroxyl benzoic esters by using density functional theory (DFT) B3LYP method of quantum chemistry and used stepwise multiple linear regression to select the descriptors and to generate the best prediction model that relates the structural features to inhibitory activity
support vector machine (SVM) has shown obvious advantages in the Quantitative Structure-Activity Relationship (QSAR) research, but QSAR study of the compound of hydroxyl benzoic esters is confined to the linear model at present; there is no literature on the nonlinear QSAR analysis of the system
From the perspective of quantum chemistry to study the relationship between the structure and properties of compound, the effective antimicrobial groups of preservative can be explained in essence [37]

Summary

Introduction

Zhang et al [22] took the benzene compounds as the research object, combining the molecular structure of the quantitative description with MLR or nonlinear regression statistical methods SVM, to build successfully the acute toxicity QSAR models and mutagenic QSAR models of benzene compounds. Qiu et al [24] optimized the molecular structures of eleven kinds of phydroxyl benzoic esters by using density functional theory (DFT) B3LYP method of quantum chemistry and used stepwise multiple linear regression to select the descriptors and to generate the best prediction model that relates the structural features to inhibitory activity. SVM has shown obvious advantages in the QSAR research, but QSAR study of the compound of hydroxyl benzoic esters is confined to the linear model at present; there is no literature on the nonlinear QSAR analysis of the system. We obtain the structure-activity relationship between the molecular structural parameters and the antibacterial activity of Escherichia coli under the most stable configuration, which provides a basis of predicting the antibacterial activity of similar compounds

Data Preparation

Establishment of Models

Results

Discussion and Conclusion