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.
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.).
Ming
M
Hao
Yan
Y
Li
Yonghua
Y
Wang
Shuwei
S
Zhang
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