Drug–target interaction prediction: databases, web servers and computational models

Abstract

Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.

Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review

“…A potential DTI can be determined by the predicted class label. There are other possible solutions, and some recent reviews focused on the technical aspect of mathematical or statistical methods (12)(13)(14)(15)(16)(17). Regardless, prior knowledge of drugs, targets, and their interactions is required for all in silico method development.…”

Drug–target interaction prediction with Bipartite Local Models and hubness-aware regression

“…Recent approaches are based on matrix factorization [12] [14] [52], restricted Boltzmann machines [48], network-based inference [9] [11] [35] [43], positive-unlabeled learning [22] and the integration of multiple sources of information [33] [42]. See also [32] and [37] for excellent surveys.…”

SDTRLS: Predicting Drug-Target Interactions for Complex Diseases Based on Chemical Substructures

“…These methods are mainly divided into three categories, including basic network-based models, machine learningbased models, and other approaches based on similarity [28].…”