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Indexed by:期刊论文

Date of Publication:2010-09-01

Journal:INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

Included Journals:PubMed、SCIE、Scopus

Volume:11

Issue:9

Page Number:3413-3433

ISSN No.:1422-0067

Key Words:protein kinase C theta; Random Forest; Partial Least Square; Support Vector Machine

Abstract:This work is devoted to the prediction of a series of 208 structurally diverse PKC theta inhibitors using the Random Forest (RF) based on the Mold(2) molecular descriptors. The RF model was established and identified as a robust predictor of the experimental pIC(50) values, producing good external R-pred(2) 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 PKC theta inhibitory activity. We hope that the developed RF model will be helpful in the screening and prediction of novel unknown PKC theta inhibitory activity.