International Journal of Molecular Sciences | |
Prediction of PKCθ Inhibitory Activity Using the Random Forest Algorithm | |
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
【 授权许可】
CC BY
© 2010 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190052209ZK.pdf | 382KB | download |