There usually exist the nonlinear quantitative structure-activity relationships ( QSAR) of drug and significant correlation among structure parameters of the drug. Sometimes, the multicollinearity is even formed among the structure parameters. Thus, the ideal regression model of QSAR with high predicting correctness can't be obtained by the method of global linear regression. At the same time, the benefits of QSAR models are valued by their predicting abilities. An adapting partial least square regression (APLSR) was proposed to model the drug's QSAR. In order to obtain the QSAR model with high predicting correctness for some predicting sample, the different predicting contribution ratio of modeling samples for the predicting sample was taken into account as well as the number of latent variables by APLSR. When APLSR was employed for the predicting sample, each modeling sample was weighted according to its different ratio of predicting contribution for the predicting sample and the optimal number of the latent variables was obtained according to the predicting ability of model. Finally, a typical example of modeling the QSAR of substituted aromatic sulfur derivatives was employed to verify the effectiveness of APLSR. The satisfactory result was obtained.
Chinese Journal of Analytical Chemistry
partial least square regression
substituted aromatic sulfur derivatives
quantitative structure-activity relationship