AMix-1 protein model uses Bayesian Flow Networks for enhanced design

Researchers have developed AMix-1, a protein foundation model utilizing Bayesian Flow Networks and a novel training methodology. This model demonstrates scalable pretraining, emergent capabilities, and effective in-context learning through multiple sequence alignments. AMix-1 has successfully designed an improved protein variant with a 50x activity increase and incorporates an evolutionary test-time scaling algorithm for enhanced in silico directed evolution. AI

影响 Introduces a new foundation model for protein design with potential to accelerate lab-in-the-loop engineering.

排序理由 This is a research paper describing a new model and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

• AMix-1
• Bayesian Flow Networks
• Siyu Long

• 论文
• 模型发布

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