Combination of kernel PCA and linear support vector machine for modeling a nonlinear relationship between bioactivity and molecular descriptors

• Published in: Journal of chemometrics Vol. 25; no. 2; pp. 92 - 99
• Main Authors: Fu, Guang-Hui, Cao, Dong-Sheng, Xu, Qing-Song, Li, Hong-Dong, Liang, Yi-Zeng
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.02.2011; Wiley; Wiley Subscription Services, Inc
• Subjects: Algorithms; Biochemistry; Chemistry; Classification; Comparative analysis; de-noising; Exact sciences and technology; General and physical chemistry; General. Nomenclature, chemical documentation, computer chemistry; kernel methods; kernel principal component analysis (KPCA); Kernels; Mathematical models; Molecular structure; Nonlinearity; Performance evaluation; Principal components analysis; structure-activity relationship (SAR); Support vector machines; support vector machines (SVMs); Synthetic aperture radar; Theory of reactions, general kinetics. Catalysis. Nomenclature, chemical documentation, computer chemistry; Gradient; Support; Prediction; Algorithm; Modeling; de-noising; Structure activity relation; structure-activity relationship (SAR); Classification; kernel methods; Models; kernel principal component analysis (KPCA); Structure; Chemometrics; Principal component analysis; support vector machines (SVMs)
• ISSN: 0886-9383; 1099-128X; 1099-128X
• DOI: 10.1002/cem.1364

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. 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). 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.