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论文类型：期刊论文

发表时间：2010-09-01

发表刊物：INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES

收录刊物：PubMed、SCIE、Scopus

卷号：11

期号：9

页面范围：3413-3433

ISSN号：1422-0067

关键字：protein kinase C theta; Random Forest; Partial Least Square; Support Vector Machine

摘要：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.