DrugRPE: Random projection ensemble approach to drug-target interaction prediction

Abstract

Drug-target interaction is key in drug discovery. Since the determination of drug-target interactions is costly and time-consuming by in vitro experiments, computational method is a complement to determine the interactions. To address the issue, a random projection ensemble approach is proposed. First, drug-compounds are encoded with feature descriptors by software “PaDEL-Descriptor”. Second, target proteins are encoded with physiochemical properties of amino acids, where the 34 relatively independent physiochemical properties are extracted from 544 properties in AAindex1 database. Random projection on the vector of drug-target pair with different dimensions can project the original space onto a reduced one and thus yield a transformed vector with a fixed dimension. Several random projections build an ensemble REPTree system. Experimental results show that our method significantly outperforms and runs faster than other state-of-the-art drug-target predictors, on the commonly used drug-target benchmark sets.