SynGFN: learning across chemical space with generative flow-based molecular discovery
8.0
来源：
Nature
关键字：
AI brain science
发布时间：
2025-11-13 23:33
摘要：
SynGFN is a novel generative flow-based model that enhances molecular discovery by simulating chemical reactions. It significantly accelerates the design-make-test-analyze cycle, enabling the identification of diverse and synthesizable molecules. The model has shown potential in designing inhibitors for neuropsychiatric disorders, indicating its relevance in drug discovery. Developed by a team from Zhejiang University and Lepu Medical Technology, SynGFN could transform approaches to molecular design and optimization.
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关键证据
SynGFN features a hierarchically pretrained policy network that accelerates learning across diverse distributions of desirable molecules.
Demonstrates SynGFN’s potential impacts by designing inhibitors for GluN1/GluN3A, a therapeutic target for neuropsychiatric disorders.
The model enables exploration of a chemical space up to an order of magnitude larger than other synthesis-aware generative models.
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AI评分总结
SynGFN is a novel generative flow-based model that enhances molecular discovery by simulating chemical reactions. It significantly accelerates the design-make-test-analyze cycle, enabling the identification of diverse and synthesizable molecules. The model has shown potential in designing inhibitors for neuropsychiatric disorders, indicating its relevance in drug discovery. Developed by a team from Zhejiang University and Lepu Medical Technology, SynGFN could transform approaches to molecular design and optimization.