Du H, Wang J, Hu Z, Yao X, Zhang X. Prediction of fungicidal activities of rice blast disease based on least-squares support vector machines and project pursuit regression.
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2008;
56:10785-10792. [PMID:
18950187 DOI:
10.1021/jf8022194]
[Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
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.
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