Prediction of Fungicidal Activities of Rice Blast Disease Based on Least-Squares Support Vector Machines and Project Pursuit Regression

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• 作者：Hongying Du ; Jie Wang ; Zhide Hu ; Xiaojun Yao ; Xiaoyun Zhang
• 刊名：Journal of Agricultural and Food Chemistry
• 出版年：2008
• 出版时间：November 26, 2008
• 年：2008
• 卷：56
• 期：22
• 页码：10785-10792
• 全文大小：330K
• 年卷期：v.56,no.22(November 26, 2008)
• ISSN：1520-5118

文摘

Three machine learning methods, genetic algorithm-multilinear regression (GA-MLR), least-squares support vector machine (LS-SVM), and project pursuit regression (PPR), were used to investigate the relationship between thiazoline derivatives and their fungicidal activities against the rice blast disease. The GA-MLR method was used to select the most appropriate molecular descriptors from a large set of descriptors, which were only calculated from molecular structures, and develop a linear quantitative structure−activity relationship (QSAR) model at the same time. On the basis of the selected descriptors, the other two more accurate models (LS-SVM and PPR) were built. Both the linear and nonlinear modes gave good prediction results, but the nonlinear models afforded better prediction ability, which meant that the LS-SVM and PPR methods could simulate the relationship between the structural descriptors and fungicidal activities more accurately. The results show that the nonlinear methods (LS-SVM and PPR) could be used as good modeling tools for the study of rice blast. Moreover, this study provides a new and simple but efficient approach, which should facilitate the design and development of new compounds to resist the rice blast disease.