Drug-Target interaction prediction through Domain-Tuned Network Based Inference

Motivation: The identification of Drug-Target Interaction (DTI) represents a costly and time consuming step in the drug discovery and design. Computational methods capable to predict reliable DTI play an important role in the field. Recently recommendation methods relying on Network Based Inference (NBI) have been proposed. However, such approaches implement naive topology based inference and do not take into account important features within the drug-target domain.Results: In this paper we present a new Network Based Inference method, called Domain Tuned-Hybrid (DT-Hybrid), which extends a well establish recommendation technique by domain-based knowledge including drugs and targets similarity. DT-Hybrid has been extensively tested using the last version of experimentally validated drug-target interaction database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly show that DT-Hybrid is capable of predicting more reliable drug-target interactions.Availability: DT-Hybrid has been developed in R, and is available, along with all the results on the predictions, through an R package at the following url http://sites.google.com/site/ehybridalgo/