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IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction
🤗 Upvotes: 32 | q-bio.BM
Authors:
The IntFold Team, Leon Qiao, Wayne Bai, He Yan, Gary Liu, Nova Xi, Xiang Zhang
Title:
IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction
Arxiv:
http://arxiv.org/abs/2507.02025v1
Abstract:
We introduce IntFold, a controllable foundation model for both general and specialized biomolecular structure prediction. IntFold demonstrates predictive accuracy comparable to the state-of-the-art AlphaFold3, while utilizing a superior customized attention kernel. Beyond standard structure prediction, IntFold can be adapted to predict allosteric states, constrained structures, and binding affinity through the use of individual adapters. Furthermore, we introduce a novel confidence head to estimate docking quality, offering a more nuanced assessment for challenging targets such as antibody-antigen complexes. Finally, we share insights gained during the training process of this computationally intensive model.
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By Jingwen Liang, Gengyu Wang🤗 Upvotes: 32 | q-bio.BM
Authors:
The IntFold Team, Leon Qiao, Wayne Bai, He Yan, Gary Liu, Nova Xi, Xiang Zhang
Title:
IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction
Arxiv:
http://arxiv.org/abs/2507.02025v1
Abstract:
We introduce IntFold, a controllable foundation model for both general and specialized biomolecular structure prediction. IntFold demonstrates predictive accuracy comparable to the state-of-the-art AlphaFold3, while utilizing a superior customized attention kernel. Beyond standard structure prediction, IntFold can be adapted to predict allosteric states, constrained structures, and binding affinity through the use of individual adapters. Furthermore, we introduce a novel confidence head to estimate docking quality, offering a more nuanced assessment for challenging targets such as antibody-antigen complexes. Finally, we share insights gained during the training process of this computationally intensive model.