期刊
CHEMICAL BIOLOGY & DRUG DESIGN
卷 73, 期 2, 页码 244-252出版社
WILEY-BLACKWELL
DOI: 10.1111/j.1747-0285.2008.00764.x
关键词
MIA-QSAR; regression methods; variable selection; WEE1 inhibitors
资金
- Fundacao de Amparo a Pesquisa de Minas Gerais (FAPEMIG) [415/06]
- Conselho Nacional de Pesquisa (CNPq)
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.
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