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27 August 2024 A deep learning model of BACE-1 inhibitors to reveal molecular interactions using graph neural networks and convolutional neural networks
Yuzhe Song, Han Zhou, Jiaqi Peng, Lu Wang, Xiumin Shi
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Proceedings Volume 13252, Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024); 132521Q (2024) https://doi.org/10.1117/12.3044287
Event: 2024 Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), 2024, Kaifeng, China
Abstract
Significant emphasis has been placed on advancing therapeutic interventions and medicines to treat Alzheimer's disease, the leading cause of dementia. BACE1 inhibitors have shown considerable promise in reducing amyloid-β levels in the brain and potentially halting the progression of Alzheimer disease. However, human clinical trials are fraught with risk and exorbitant cost. In addressing these challenges, this investigation has developed a deep learning model for the prediction of interactions between BACE1 inhibitors and ligand. The model represents compounds as molecular graphs and SMILES strings, which are then processed through Graph Neural Network and Convolutional Neural Network channels, respectively. This approach allows comprehensive prediction of IC50 values and classification of compound activity with the BACE1 inhibitor. The model can be used as a convenient tool for the development of BACE1 inhibitors and also for virtual screening of molecules to identify potential inhibitors.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Yuzhe Song, Han Zhou, Jiaqi Peng, Lu Wang, and Xiumin Shi "A deep learning model of BACE-1 inhibitors to reveal molecular interactions using graph neural networks and convolutional neural networks", Proc. SPIE 13252, Fourth International Conference on Biomedicine and Bioinformatics Engineering (ICBBE 2024), 132521Q (27 August 2024); https://doi.org/10.1117/12.3044287
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KEYWORDS
Alzheimer disease
Deep learning
Neural networks
Data modeling
Convolutional neural networks
Molecular interactions
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