Quantitative structure–activity relationship study of influenza virus neuraminidase A/PR/8/34 (H1N1) inhibitors by genetic algorithm feature selection and support vector regression

Abstract

The quantitative structure–activity relationship (QSAR) for the prediction of the activity of two different scaffolds of 108 influenza neuraminidase A/PR/8/34 (H1N1) inhibitors was investigated. A feature selection method, which combines Genetic Algorithm with Partial Least Square (GA–PLS), was applied to select proper descriptor subset for QSAR modeling in a linear model. Then Genetic Algorithm-Support Vector Machine coupled approach (GA–SVM) was first used to build the nonlinear models with nine GA–PLS selected descriptors. With the SVM regression model, the corresponding correlation coefficients (R) of 0.9189 for the training set, 0.9415 for the testing set and 0.9254 for the whole data set were achieved respectively. The two proposed models gained satisfactory prediction results and can be extended to other QSAR studies.