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

TLDR

Identifying drug–target interactions is costly and time‑consuming, so computational methods—particularly network‑based inference approaches—are sought, yet existing NBI methods rely on naive topology and ignore domain‑specific features. The study introduces DT‑Hybrid, a domain‑tuned network‑based inference method that incorporates drug and target similarity. DT‑Hybrid was evaluated on the latest experimentally validated DTI database from DrugBank. DT‑Hybrid outperforms recent NBI methods in predicting reliable drug–target interactions. DT‑Hybrid is available as an R package at http://sites.google.com/site/ehybridalgo/ (contact [e-mail]; supplementary data at Bioinformatics online).

Abstract

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/. Contact: [e-mail] Supplementary information: Supplementary data are available at Bioinformatics online.