Paper · arXiv 2507.02025
IntFold: A Controllable Foundation Model for General and Specialized Biomolecular Structure Prediction
The IntFold Team, Leon Qiao, Wayne Bai, He Yan, Gary Liu, Nova Xi, Xiang Zhang
IntFold, a foundation model with a customized attention kernel, achieves accuracy comparable to AlphaFold3 and can predict various biomolecular structures and binding affinities using adapters and a confidence head for docking quality.
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.