Starkly Speaking: SeedFold: Scaling Biomolecular Structure Prediction
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Hannes Stärk
Feb 1, 2026, 5:10:24 PMFeb 1
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Hi together,
Tomorrow, we will discuss the first paper to have better structure prediction performance on multiple tasks than AF3!
Speaker:
Yi Zhou and Chan Lu from the ByteDance Seed team!
Paper:
SeedFold: Scaling Biomolecular Structure Prediction https://arxiv.org/abs/2512.24354 (Yi Zhou, Chan Lu, Yiming Ma, Wei Qu, Fei Ye, Kexin Zhang, Lan Wang, Minrui Gui, Quanquan Gu)
Highly accurate biomolecular structure prediction is a key component of developing biomolecular foundation models, and one of the most critical aspects of building foundation models is identifying the recipes for scaling the model. In this work, we present SeedFold, a folding model that successfully scales up the model capacity. Our contributions are threefold: first, we identify an effective width-scaling strategy for the Pairformer to increase representation capacity; second, we introduce a novel linear triangular attention that reduces computational complexity to enable efficient scaling; finally, we construct a large-scale distillation dataset to substantially enlarge the training set. Experiments on FoldBench show that SeedFold outperforms AlphaFold3 on most protein-related tasks.
Meeting Details:
Every Monday at 9:00 PT / 12:00 ET / 18:00 CE(S)T
https://zoom.us/j/5775722530?pwd=ZzlGTXlDNThhUDZOdU4vN2JRMm5pQT09
Yi Zhou and Chan Lu from the ByteDance Seed team!
Paper:
SeedFold: Scaling Biomolecular Structure Prediction https://arxiv.org/abs/2512.24354 (Yi Zhou, Chan Lu, Yiming Ma, Wei Qu, Fei Ye, Kexin Zhang, Lan Wang, Minrui Gui, Quanquan Gu)
Highly accurate biomolecular structure prediction is a key component of developing biomolecular foundation models, and one of the most critical aspects of building foundation models is identifying the recipes for scaling the model. In this work, we present SeedFold, a folding model that successfully scales up the model capacity. Our contributions are threefold: first, we identify an effective width-scaling strategy for the Pairformer to increase representation capacity; second, we introduce a novel linear triangular attention that reduces computational complexity to enable efficient scaling; finally, we construct a large-scale distillation dataset to substantially enlarge the training set. Experiments on FoldBench show that SeedFold outperforms AlphaFold3 on most protein-related tasks.
Meeting Details:
Every Monday at 9:00 PT / 12:00 ET / 18:00 CE(S)T
https://zoom.us/j/5775722530?pwd=ZzlGTXlDNThhUDZOdU4vN2JRMm5pQT09
Meeting Details:
Every Monday at 9:00 PT / 12:00 ET / 18:00 CE(S)T
https://zoom.us/j/5775722530?pwd=ZzlGTXlDNThhUDZOdU4vN2JRMm5pQT09
Slack Workspace for discussion and paper voting:
https://join.slack.com/t/logag/shared_invite/zt-2zuxi7gd1-rLUgxg6gnCkhO7WlRsyElg
All information: Schedule of upcoming papers, recordings, mailing list:
https://portal.valencelabs.com/starklyspeaking
Hannes Stärk
Website: https://hannes-stark.com
PhD student at MIT
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