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Int. J. Mol. Sci. 2010, 11(9), 3413-3433; doi:10.3390/ijms11093413

Prediction of PKCθ Inhibitory Activity Using the Random Forest Algorithm

School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116012, China
Center of Bioinformatics, Northwest A&F University, Yangling, Shaanxi 712100, China
Author to whom correspondence should be addressed.
Received: 19 July 2010 / Revised: 24 August 2010 / Accepted: 3 September 2010 / Published: 20 September 2010
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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

This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Hao, M.; Li, Y.; Wang, Y.; Zhang, S. Prediction of PKCθ Inhibitory Activity Using the Random Forest Algorithm. Int. J. Mol. Sci. 2010, 11, 3413-3433.

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