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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

1,* , 2
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
* 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.
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) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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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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