Qiu W, Liang Q, Yu L, Xiao X, Qiu W, Lin W. LSTM-SAGDTA: Predicting Drug-Target Binding Affinity with an Attention Graph Neural Network and LSTM Approach.
Curr Pharm Des 2024;
30:CPD-EPUB-138368. [PMID:
38323613 PMCID:
PMC11071654 DOI:
10.2174/0113816128282837240130102817]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 01/14/2024] [Accepted: 01/19/2024] [Indexed: 02/08/2024]
Abstract
INTRODUCTION
Drug development is a challenging and costly process, yet it plays a crucial role in improving healthcare outcomes. Drug development requires extensive research and testing to meet the demands for economic efficiency, cures, and pain relief.
METHODS
Drug development is a vital research area that necessitates innovation and collaboration to achieve significant breakthroughs. Computer-aided drug design provides a promising avenue for drug discovery and development by reducing costs and improving the efficiency of drug design and testing.
RESULTS
In this study, a novel model, namely LSTM-SAGDTA, capable of accurately predicting drug-target binding affinity, was developed. We employed SeqVec for characterizing the protein and utilized the graph neural networks to capture information on drug molecules. By introducing self-attentive graph pooling, the model achieved greater accuracy and efficiency in predicting drug-target binding affinity.
CONCLUSION
Moreover, LSTM-SAGDTA obtained superior accuracy over current state-of-the-art methods only by using less training time. The results of experiments suggest that this method represents a highprecision solution for the DTA predictor.
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