Network-Based Methods for Prediction of Drug-Target Interactions

Abstract

Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.

Keywords: drug repurposing; drug-target interaction; network-based method; systems pharmacology; systems toxicology; target prediction.

FIGURE 1

Examples of predicting potential targets for an example drug highlighted with yellow shadow via three network-based methods: (A) network-based inference (NBI), (B) substructure-drug-target network-based inference (SDTNBI), (C) balanced substructure-drug-target network-based inference (bSDTNBI). Drugs, targets and substructures were represented as capsules, ribbons and structural fragments, respectively.