[1]LIU Xiaoguang,LI Mei.A survey of deep learning-based drug-target interaction prediction[J].CAAI Transactions on Intelligent Systems,2024,19(3):494-524.[doi:10.11992/tis.202308024]
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A survey of deep learning-based drug-target interaction prediction
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
19
Number of periods:
2024 3
Page number:
494-524
Column:
综述
Public date:
2024-05-05
- Title:
- A survey of deep learning-based drug-target interaction prediction
- Keywords:
- drug-target interaction; artificial intelligence; machine learning; deep learning; drug discovery and development; graph neural network; heterogeneous network; representation learning
- CLC:
- TP18
- DOI:
- 10.11992/tis.202308024
- Abstract:
- The development of novel drugs is a time-consuming, labor-consuming, and costly process with the overall success rate no more than 10%. The prediction of drug-target interactions (DTIs) is fundamental for drug screening and drug repositioning. Accurate DTI prediction can significantly narrow down the screening of drug candidates and accelerate the drug discovery process. The traditional experimental method for identifying DTIs is tedious and expensive and accompanied by certain blindness, which restricts it from large-scale DTI identification. Recently, applying machine learning especially deep learning techniques to DTI prediction has become the mainstream. Although a series of methods have been proposed in the last decade, DTI prediction is still a material-intensive and long-term work, and is challenging to researchers. In this survey, we review literature related to DTI prediction, and summarize the methodologies, evaluation indicators, and data sources used in these works. We also analyze the shortcomings of existing works and propose future prospects. Our motivation is to help researches dedicated to drug discovery and development to have a comprehensive understanding on the latest progress of DTI prediction so as to improve their research efficiency and research quality.
- References:
-
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