Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors. Guha, R. & Jurs, P. J.~Chem.~Inf.~Comput.~Sci., 44(6):2179--2189, Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA., 2004.
Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of PDGFR Inhibitors. [link]Paper  doi  abstract   bibtex   
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 (R2 = 0.93) and a prediction set error of 0.32 log(IC50) units (R2 = 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.
@article{Guha:2004ab,
	Abstract = {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 (R2 = 0.93) and a prediction set error of 0.32 log(IC50) units (R2 = 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.},
	Address = {Department of Chemistry, Penn State University, University Park, Pennsylvania 16802, USA.},
	Author = {Guha, Rajarshi and Jurs, P.C.},
	Date-Added = {2007-12-11 17:01:03 -0500},
	Date-Modified = {2008-08-07 21:56:47 -0400},
	Doi = {10.1021/ci049849f},
	Journal = {J.~Chem.~Inf.~Comput.~Sci.},
	Keywords = {Linear Models; Models, Chemical; Models, Molecular; Molecular Structure; Nonlinear Dynamics; Quantitative Structure-Activity Relationship; Receptors, Platelet-Derived Growth Factor/*antagonists \& inhibitors/*chemistry},
	Local-Url = {file://localhost/Users/rguha/Documents/articles/ci049849f.pdf},
	Number = {6},
	Pages = {2179--2189},
	Title = {Development of Linear, Ensemble, and Nonlinear Models for the Prediction and Interpretation of the Biological Activity of a Set of {PDGFR} Inhibitors.},
	Url = {http://dx.doi.org/10.1021/ci049849f},
	Volume = {44},
	Year = {2004},
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