GPCR–drug interactions prediction using random forest with drug-association-matrix-based post-processing procedure

A new sequence-based predictor called TargetGDrug is designed and implemented for predicting GPCR–drug interactions. The evolutionary feature of GPCR sequence and the wavelet-based molecular fingerprint feature of drug are integrated to form the combined feature of a GPCR–drug pair; the combined feature is fed to a trained random forest classifier to perform initial prediction; finally, a novel drug-association-matrix-based post-processing procedure is applied to reduce potential false positive or false negative of the initial prediction. The webserver is freely available for academic use at http://csbio.njust.edu.cn/bioinf/TargetGDrug.