Survey of network-based approaches of drug-target interaction prediction

Drug repositioning is a promising strategy for drug design and discovery. The key step of drug repositioning is computational prediction of drug-target interactions (DTIs). Recently, machine learning methods have provided efficient tools to predict DTIs. These supervised methods require a sufficient amount of training data including negative samples to increase prediction accuracy. The network-based approaches also provide an effective solution for DTI prediction. These models use a bipartite graph where each node represents either a drug or a target and each edge is an interaction. The network-based approaches then adopt graph-theoretic techniques based on the topological features of the DTI network. Using the benchmark DTI data set integrated from multiple resources, their performance can be assessed by cross-validation. The AUC and AUPR results show the network-based approaches have competitive accuracy. Improving data quality, for example, precise measurement of structural similarity between chemical compounds, is the most significant issue for current network-based DTI prediction.