Agnostic Biomolecular Binding Affinity Prediction via Frame Averaging Graph Transformer

Predicting binding affinity between biomolecules is a critical task in drug discovery, where deep learning methods have achieved significant progress. However, many existing approaches employ modality-specific network architectures, limiting their direct applicability across diverse biomolecular interaction types. In this work, we propose F3Affinity, a novel structure-based graph transformer that is agnostic to biomolecular interaction type in its architectural design. Leveraging a frame averaging technique, our model flexibly learns SE(3)-invariant representations of input structures. We further demonstrate that F3Affinity can be independently applied to various binding affinity prediction benchmarks without requiring any pre-trained embeddings, including protein-ligand, protein-protein, and protein-nucleic acid interactions, and achieving competitive performance. These results validate its broad applicability and effectiveness across multiple biomolecular interaction types.