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1

School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116012, China

2

Center of Bioinformatics, Northwest A&F University, Yangling, Shaanxi 712100, China





*

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Abstract This work is devoted to the prediction of a series of 208 structurally diverse PKCθ inhibitors using the Random Forest RF based on the Mold2 molecular descriptors. The RF model was established and identified as a robust predictor of the experimental pIC50 values, producing good external R2pred of 0.72, a standard error of prediction SEP of 0.45, for an external prediction set of 51 inhibitors which were not used in the development of QSAR models. By using the RF built-in measure of the relative importance of the descriptors, an important predictor—the number of group donor atoms for H-bonds with N and O―has been identified to play a crucial role in PKCθ inhibitory activity. We hope that the developed RF model will be helpful in the screening and prediction of novel unknown PKCθ inhibitory activity. View Full-Text

Keywords: protein kinase C θ; Random Forest; Partial Least Square; Support Vector Machine protein kinase C θ; Random Forest; Partial Least Square; Support Vector Machine





Autor: Ming Hao 1, Yan Li 1,* , Yonghua Wang 2 and Shuwei Zhang 1

Fuente: http://mdpi.com/



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