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Hierarchical graph representation learning for the prediction of drug-target binding affinity.
- Source :
-
Information Sciences . Oct2022, Vol. 613, p507-523. 17p. - Publication Year :
- 2022
-
Abstract
- • A novel hierarchical graph representation learning model for drug-target binding affinity prediction. • Represents the drug-target binding affinity data as a hierarchical graph. • Hierarchically integrates coarse- and fine-level information in a coarse-to-fine manner. Computationally predicting drug-target binding affinity (DTA) has attracted increasing attention due to its benefit for accelerating drug discovery. Currently, numerous deep learning-based prediction models have been proposed, often with a biencoder architecture that commonly focuses on how to extract expressive representations for drugs and targets but overlooks modeling explicit drug-target interactions. However, known DTA can provide underlying knowledge about how the drugs interact with targets that is beneficial for predictive accuracy. In this paper, we propose a novel hierarchical graph representation learning model for DTA prediction, named HGRL-DTA. The main contribution of our model is to establish a hierarchical graph learning architecture to integrate the coarse- and fine-level information from an affinity graph and drug/target molecule graphs, respectively, in a well-designed coarse-to-fine manner. In addition, we design a similarity-based representation inference method to infer coarse-level information when it is unavailable for new drugs or targets under the cold start scenario. Comprehensive experimental results under four scenarios across two benchmark datasets indicate that HGRL-DTA outperforms the state-of-the-art models in almost all cases. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00200255
- Volume :
- 613
- Database :
- Academic Search Index
- Journal :
- Information Sciences
- Publication Type :
- Periodical
- Accession number :
- 159928220
- Full Text :
- https://doi.org/10.1016/j.ins.2022.09.043