Prediction of cytochrome P450 inhibition using ensemble of extreme learning machine

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP) inhibition play crucial roles in drug discovery. It is urgent and challenging to develop computational methods to efficiently and accurately predict the inhibitive effect of a compound against a specific CYP isoform. In this work we present a novel EELM (ensemble of extreme learning machine) model to predict CYP inhibition. Particularly, extreme learning machine (ELM) and fingerprint descriptors are firstly used to build the weak learning machines. And then EELM is constructed by combining the outputs of each individual ELM using majority voting strategy. Experimental results demonstrate that the proposed method yields good results compared with the existing methods.