Journal
JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES
Volume 44, Issue 6, Pages 2179-2189Publisher
AMER CHEMICAL SOC
DOI: 10.1021/ci049849f
Keywords
-
Ask authors/readers for more resources
A QSAR modeling Study has been done with a set of 79 piperazyinylquinazoline analogues which exhibit PDGFR inhibition. Linear regression and nonlinear computational neural network models were developed. The regression model was developed with a focus on interpretative ability using a PLS technique. However, it also exhibits a good predictive ability after outlier removal. The nonlinear CNN model had superior predictive ability compared to the linear model with a training,, set error of 0.22 log(IC50) units (R-2 = 0.93) and a prediction set error of 0.32 log(IC50) units (R-2 = 0.61). A random forest model was also developed to provide an alternate measure of descriptor importance. This approach ranks descriptors, and its results confirm the importance of specific descriptors as characterized by the PLS technique. In addition the neural network model contains the two most important descriptors indicated by the random forest model.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available