Drug-target interaction prediction through domain-tuned network-based inference

Abstract

Motivation: The identification of drug-target interaction (DTI) represents a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting 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 article, we present a new NBI method, called domain tuned-hybrid (DT-Hybrid), which extends a well-established recommendation technique by domain-based knowledge including drug and target similarity. DT-Hybrid has been extensively tested using the last version of an experimentally validated DTI database obtained from DrugBank. Comparison with other recently proposed NBI methods clearly shows that DT-Hybrid is capable of predicting more reliable DTIs.

Availability: DT-Hybrid has been developed in R and it is available, along with all the results on the predictions, through an R package at the following URL: http://sites.google.com/site/ehybridalgo/.

Fig. 1.

Comparison between DT-Hybrid, Hybrid, and NBI by means of receiver operating characteristic (ROC) curves, computed for the top-L places of the recommendation lists, which were built on the complete DrugBank dataset

Fig. 2.

Comparison between DT-Hybrid, Hybrid and NBI by means of receiver operating characteristic (ROC) curves, computed for the top-30 places of the recommendation lists, which were built on the four datasets (enzymes, ion channels, GPCRs and nuclear receptors)