LSTM-SAGDTA: Predicting Drug-target Binding Affinity with an Attention Graph Neural Network and LSTM Approach
- Авторы: Qiu W.1, Liang Q.1, Yu L.1, Xiao X.1, Qiu W.1, Lin W.1
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Учреждения:
- School of Information Engineering, Jingdezhen Ceramic University
- Выпуск: Том 30, № 6 (2024)
- Страницы: 468-476
- Раздел: Immunology, Inflammation & Allergy
- URL: https://vestnikugrasu.org/1381-6128/article/view/646004
- DOI: https://doi.org/10.2174/0113816128282837240130102817
- ID: 646004
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Аннотация
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.
Об авторах
Wenjing Qiu
School of Information Engineering, Jingdezhen Ceramic University
Email: info@benthamscience.net
Qianle Liang
School of Information Engineering, Jingdezhen Ceramic University
Email: info@benthamscience.net
Liyi Yu
School of Information Engineering, Jingdezhen Ceramic University
Email: info@benthamscience.net
Xuan Xiao
School of Information Engineering, Jingdezhen Ceramic University
Email: info@benthamscience.net
Wangren Qiu
School of Information Engineering, Jingdezhen Ceramic University
Email: info@benthamscience.net
Weizhong Lin
School of Information Engineering, Jingdezhen Ceramic University
Автор, ответственный за переписку.
Email: info@benthamscience.net
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