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DeepTransformer: Node Classification Research of a Deep Graph Network on an Osteoporosis Graph based on
Yixin Liu1, Guowei Jiang2, Miaomiao Sun3
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
This study introduces an AI-powered deep graph neural network, DeepTransformer, to accelerate osteoporosis drug discovery. The model significantly reduces development time and cost, aiding patients and families.
Area of Science:
- Computational biology
- Artificial intelligence in drug discovery
- Graph neural networks
Background:
- Osteoporosis (OP) is a prevalent condition in the elderly.
- Current drug development for OP is time-consuming and expensive.
Purpose of the Study:
- To develop an AI-driven approach for novel osteoporosis drug discovery.
- To provide new avenues for research and development in OP therapeutics.
Main Methods:
- Constructed a novel osteoporosis graph (OPGraph) from extensive OP data.
- Proposed DeepTransformer, a deep graph neural network based on GraphTransformer with residual connections.
Main Results:
- DeepTransformer achieved high performance on OPGraph, with AUC of 0.9916 and AUPR of 0.9911.
- In vitro validation confirmed the efficacy of two predicted compounds, Puerarin and Aucubin.
Conclusions:
- The AI model effectively accelerates osteoporosis drug development, reducing time and costs.
- This approach alleviates the economic burden associated with OP complications.
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