Improving Prediction of Drug-Target Binding Affinity using Attention Mechanism and Bi-Directional Long Short-Term Memory
Pages 232 - 237
Abstract
The successful identification of drug-target interactions (DTIs) plays an important role in the drug discovery process and drug repurposing. However, most methods for DTIs prediction ignore a continuous value, namely binding affinity, and it is an essential piece of information about protein-ligand interactions. In this study, we propose a deep learn-based prediction model called HABiLSTM-DTA, which is designed for prediction of drug-target affinity. The main innovation is the application of the attention mechanism to model the complex interactions between drug molecules and protein sequences. We also use convolutional layers and bi-directional long short-term memory to learn their feature representations. We evaluate the proposed model on two benchmark datasets. The results show that the proposed model achieves relatively better results than other 1D sequence-based baselines, and it is an effective method for predicting drug target binding affinity.
CCS CONCEPTS•Computing methodologies∼Machine learning∼Machine learning approaches∼Neural networks
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Published In
May 2023
313 pages
ISBN:9798400700385
DOI:10.1145/3608164
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 November 2023
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- Research-article
- Research
- Refereed limited
Funding Sources
- the National Natural Science Foundation of China
- the Key open project of Key Laboratory of Data Science and Intelligence Education (Hainan Normal University), Ministry of Education
- the Key Scientific Research Projects of Department of Education of Hunan Province
- National Key R&D Program of China
Conference
ICBBT 2023
ICBBT 2023: 2023 15th International Conference on Bioinformatics and Biomedical Technology
May 26 - 28, 2023
Xi'an, China
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