SeedFold: Scaling Biomolecular Structure Prediction
Research Paper#Biomolecular Structure Prediction🔬 Research|Analyzed: Jan 3, 2026 15:36•
Published: Dec 30, 2025 17:05
•1 min read
•ArXivAnalysis
This paper presents SeedFold, a model for biomolecular structure prediction, focusing on scaling up model capacity. It addresses a critical aspect of foundation model development. The paper's significance lies in its contributions to improving the accuracy and efficiency of structure prediction, potentially impacting the development of biomolecular foundation models and related applications.
Key Takeaways
- •Introduces SeedFold, a model for biomolecular structure prediction.
- •Employs a width-scaling strategy for the Pairformer.
- •Utilizes linear triangular attention for computational efficiency.
- •Constructs a large-scale distillation dataset for training.
- •Outperforms AlphaFold3 on most protein-related tasks.
Reference / Citation
View Original"SeedFold outperforms AlphaFold3 on most protein-related tasks."