Visualization of Chemical Space Using Principal Component Analysis
B. Firdaus Begam, J. Satheesh Kumar · 2014
Principal component analysis is one of the most widely used multivariate methods to visualize chemical space in new dimension by the chemist for analysing data. In Multivariate data analysis, the relationship between two variables with more number of characteristics can be considered. PCA provides a compact view of variation in chemical data matrix which helps in creating better Quantitative Structure Activity Relationship (QSAR) model. It highlights the dominating pattern in the matrix through principal component and graphical representation. This paper focuses on mathematical aspects of principal components and role of PCA on Maybridge dataset to identify dominating hidden patterns of drug likeness based on Lipinski RO5.