CAJ | 학술논문

采用支持向量机回归(SVR)方法研究了39个麻醉药毒性的定量构效关系,基于留一法交叉验证的结果,模型的相关系数为0.970.结果表明,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(MLR)和偏最小二乘法(PLS)所得的结果.
채용지지향량궤회귀(SVR)방법연구료39개마취약독성적정량구효관계,기우류일법교차험증적결과,모형적상관계수위0.970.결과표명,소건SVR모형적정도고우역전파인공신경망락(BPANN)、다원선성회귀(MLR)화편최소이승법(PLS)소득적결과.
A quantitative structure-property relationships (QSPR) study based on support vector regression (SVR) for the toxicities (pEC50) of 39 narcotics was performed. SVR method with leave-one-out cross-validation was used for evaluating the regression models. The correlation coefficient obtained by the models was 0.970. The result showed that the prediction accuracy of SVR model was higher than those of back propagation artificial neural network (BP ANN), multiple linear regression (MLR) and partial least squares (PLS) methods.