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采用支持向量回归方法研究了1,4,2-二氮磷杂环戊-5-(硫)酮类化合物除草活性的QSAR。基于留一法交叉验证的结果,比较了支持向量机回归(SVR)与几种常用建模方法对于该类化合物除草活性的预测精度。研究表明:所建SVR模型的精度高于逆传播人工神经网络(BPANN)、多元线性回归和偏最小二乘(PLS)所得结果。
QSAR of herbicidal activity of 1,4,2-diazaphosphorin-5 (thio) ketones was studied using support vector regression method. Based on the results of the one-leave-only cross-validation, the prediction accuracy of the herbicidal activity of these compounds was compared between support vector machine regression (SVR) and several commonly used modeling methods. The results show that the accuracy of SVR model is higher than that of BPANN, PLS and multiple linear regression.