Prediction of PKCθ Inhibitory Activity Using the Random Forest Algorithm

International Journal of Molecular Sciences

Ming Hao²  Yan Li²  Yonghua Wang¹ 

[1] Center of Bioinformatics, Northwest A&F University, Yangling, Shaanxi 712100, China; E-Mail:;School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116012, China; E-Mails:

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

DOI  :  10.3390/ijms11093413

来源: mdpi

【 摘 要 】

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 R²_pred 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.

【 授权许可】

CC BY   
© 2010 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.