• kernel methods;
  • structure–activity relationship (SAR);
  • kernel principal component analysis (KPCA);
  • support vector machines (SVMs);
  • classification;
  • de-noising


In this paper, a two-step nonlinear classification algorithm is proposed to model the structure–activity relationship (SAR) between bioactivities and molecular descriptors of compounds, which consists of kernel principal component analysis (KPCA) and linear support vector machines (KPCA + LSVM). KPCA is used to remove some uninformative gradients such as noises and then exactly capture the latent structure of the training dataset using some new variables called the principal components in the kernel-defined feature space. LSVM makes full use of the maximal margin hyperplane to give the best generalization performance in the KPCA-transformed space. The combination of KPCA and LSVM can effectively improve the prediction performance compared with the linear SVM as well as two nonlinear methods. Three datasets related to different categorical bioactivities of compounds are used to evaluate the performance of KPCA + LSVM. The results show that our algorithm is competitive. Copyright © 2011 John Wiley & Sons, Ltd.