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DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network
- Source :
- Current Issues in Molecular Biology, Vol 44, Iss 5, Pp 2287-2299 (2022)
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
Abstract
- Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.
Details
- Language :
- English
- ISSN :
- 14673045 and 14673037
- Volume :
- 44
- Issue :
- 5
- Database :
- Directory of Open Access Journals
- Journal :
- Current Issues in Molecular Biology
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.4d0f0c832642bf88a9f1852679bb00
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/cimb44050155