The Applications of PCA in QSAR Studies: A case study on CCR5 antagonists

Abstract

Principal component analysis (PCA), as a well-known multivariate data analysis and data reduction technique, is an important and useful algebraic tool in drug design and discovery. PCA, in a typical quantitative structure activity relationship (QSAR) study, analyzes an original data matrix in which molecules are described by several inter-correlated quantitative dependent variables (molecular descriptors). Although extensively applied, there is disparity in the literature with respect to the applications of PCA in the QSAR studies. This study investigates the different applications of PCA in QSAR studies using a dataset including CCR5 inhibitors. The different types of pre-processing are used to compare the PCA performances. The use of PC plots in the exploratory investigation of matrix of descriptors is described. This work is also proved PCA analysis to be a powerful technique for exploring complex datasets in QSAR studies for identification of outliers. This study shows that PCA is able to easily applied to the pool of calculated structural descriptors, and also the extracted information can be used to help decide upon an appropriate harder model for further analysis.

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Most important aims of PCA in QSAR studies are reviewed in this study such as to extract the important information from the molecular descriptor matrix, to represent it as a set of new orthogonal variables called principal components (PCs), and to display the pattern of similarity of the molecules in data set of interest and of the molecular descriptors as points in maps.

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