IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction

The IntFold Team, Leon Qiao, Wayne Bai, He Yan, Liu, Nova Xi, Xiang Zhang

IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction: 31 upvotes on Hugging Face Daily Papers, #7 of 17 papers on 2025-07-04. Day-by-day upvote history. It lost 5 votes when the Hub removed votes in bulk.

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.

Paper page on Hugging Face · arXiv

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