The QSAR Modeling of Herbicide Using a Combinatorial Model Based on Support Vector Regression and K-nearest Neighbor

To improve the precision of QSAR modeling,a novel nonlinear combinatorial forecast method based on support vector regression (SVR) and K-nearest neighbor (KNN) was proposed.Kernels and descriptors optimization based on SVR were evaluated by the rule of minimum MSE value,and "multi-round enforcement optimization" was taken to illuminate the effect of retained descriptors on forecasting precision.The heterogeneity of sample set was characterized by different KNN and multiple sub-models were assembled,then combinational forecast was carried out based on leave-one-out method.The new method had been employed to study for QSAR on a series of herbicidal materials,N-phenylacetamides,and has the highest prediction precision in all reference models.It has the advantages of structural risk minimization,non-linear characteristics,avoiding the over-fit and strong generalization ability,etc.The novel combination model,so,can be widely used in QSAR.