Correlation Engine 2.0
Clear Search sequence regions


Sizes of these terms reflect their relevance to your search.

The identification of Drug-Target Interactions (DTIs) is an important process in drug discovery and medical research. However, the tradition experimental methods for DTIs identification are still time consuming, extremely expensive and challenging. In the past ten years, various computational methods have been developed to identify potential DTIs. In this paper, the identification methods of DTIs are summarized. What's more, several state-of-the-art computational methods are mainly introduced, containing network-based method and machine learning-based method. In particular, for machine learning-based methods, including the supervised and semisupervised models, have essential differences in the approach of negative samples. Although these effective computational models in identification of DTIs have achieved significant improvements, network-based and machine learning-based methods have their disadvantages, respectively. These computational methods are evaluated on four benchmark data sets via values of Area Under the Precision Recall curve (AUPR). Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.

Citation

Yijie Ding, Jijun Tang, Fei Guo. The Computational Models of Drug-target Interaction Prediction. Protein and peptide letters. 2020;27(5):348-358

Expand section icon Mesh Tags

Expand section icon Substances


PMID: 30968771

View Full Text