Prediction of PKCθ inhibitory activity using the Random Forest Algorithm.
This work is devoted to the prediction of a series of 208 structurally diverse PKCθ 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(2) (pred) 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.
Hao
Ming
M
School of Chemical Engineering, Dalian University of Technology, Dalian, Liaoning 116012, China; E-Mails: dluthm@yeah.net (M.H.); zswei@chem.dlut.edu.cn (S.Z.).
Li
Yan
Y
Wang
Yonghua
Y
Zhang
Shuwei
S
D000465:Algorithms; D006801:Humans; D007527:Isoenzymes / Q000037:antagonists & inhibitors* / Q000737:chemistry; D011493:Protein Kinase C / Q000037:antagonists & inhibitors* / Q000737:chemistry; D000074121:Protein Kinase C-theta; D047428:Protein Kinase Inhibitors / Q000737:chemistry / Q000494:pharmacology; D021281:Quantitative Structure-Activity Relationship
D007527:Isoenzymes; D047428:Protein Kinase Inhibitors; C076233:PRKCQ protein, human; D011493:Protein Kinase C; D000074121:Protein Kinase C-theta