TY - JOUR
T1 - Improvement of multivariate image analysis applied to quantitative structure-activity relationship (QSAR) analysis by using wavelet-principal component analysis ranking variable selection and least-squares support vector machine regression
T2 - QSAR study of checkpoint kinase WEE1 inhibitors
AU - Cormanich, Rodrigo A.
AU - Goodarzi, Mohammad
AU - Freitas, Matheus P.
PY - 2009/2/1
Y1 - 2009/2/1
N2 - 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.
AB - 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.
KW - MIA-QSAR
KW - Regression methods
KW - Variable selection
KW - WEE1 inhibitors
UR - http://www.scopus.com/inward/record.url?scp=58849083776&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58849083776&partnerID=8YFLogxK
U2 - 10.1111/j.1747-0285.2008.00764.x
DO - 10.1111/j.1747-0285.2008.00764.x
M3 - Article
C2 - 19207427
AN - SCOPUS:58849083776
SN - 1747-0277
VL - 73
SP - 244
EP - 252
JO - Chemical Biology and Drug Design
JF - Chemical Biology and Drug Design
IS - 2
ER -