title: 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: 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.
authors.affiliation: Departamento de Química, Universidade Federal de Lavras, CP 3037, 37200-000 Lavras, MG, Brazil.
authors.forename: Rodrigo A
authors.initials: RA
authors.lastname: Cormanich
authors.forename: Mohammad
authors.initials: M
authors.lastname: Goodarzi
authors.forename: Matheus P
authors.initials: MP
authors.lastname: Freitas
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000737:chemistry* / Q000494:pharmacology; D001185:Artificial Intelligence; D002227:Carbazoles / Q000737:chemistry* / Q000494:pharmacology*; D018797:Cell Cycle Proteins / Q000037:antagonists & inhibitors* / Q000378:metabolism; D006801:Humans; D020128:Inhibitory Concentration 50; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D009687:Nuclear Proteins / Q000037:antagonists & inhibitors* / Q000378:metabolism; D025341:Principal Component Analysis; D047428:Protein Kinase Inhibitors / Q000737:chemistry* / Q000494:pharmacology; D011505:Protein-Tyrosine Kinases / Q000037:antagonists & inhibitors* / Q000378:metabolism; D011758:Pyrroles / Q000737:chemistry* / Q000494:pharmacology*; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
chemical_list: D000970:Antineoplastic Agents; D002227:Carbazoles; D018797:Cell Cycle Proteins; D009687:Nuclear Proteins; D047428:Protein Kinase Inhibitors; D011758:Pyrroles; D011505:Protein-Tyrosine Kinases; C499349:WEE1 protein, human