Improvement of MIA-QSAR analysis by using wavelet-pca ranking variable selection and LS-SVM regression: QSAR study of checkpoint kinase WEE1 inhibitors

Abstract

Inhibition of tyrosine kinase enzyme WEE1 is an important step for the treatment of cancer. The bioactivities of a series of WEE1 inhibitors have been previously modeled through comparative molecular field analyses (CoMFA and CoMSIA), but a two‐dimensional image‐based quantitative structure–activity relationship approach has shown to be highly predictive for other compound classes. This method, called multivariate image analysis applied to quantitative structure–activity relationship, was applied here to derive quantitative structure–activity relationship models. Whilst the well‐known bilinear and multilinear partial least squares regressions (PLS and N‐PLS, respectively) correlated multivariate image analysis descriptors with the corresponding dependent variables only reasonably well, the use of wavelet and principal component ranking as variable selection methods, together with least‐squares support vector machine, improved significantly the prediction statistics. These recently implemented mathematical tools, particularly novel in quantitative structure–activity relationship studies, represent an important advance for the development of more predictive quantitative structure–activity relationship models and, consequently, new drugs.