FlashAffinity: Bridging the Accuracy-Speed Gap in Protein-Ligand Binding Affinity Prediction

Accurate prediction of protein-ligand binding affinity is central to computational drug discovery. Recent foundation models, such as Boltz-2, have achieved remark-able accuracy, but their high computational cost poses a major barrier to large-scale virtual screening. We address this challenge by introducing a lightweight structure-based virtual screening model, FlashAffinity, that achieves similar performance as Boltz-2 in affinity prediction and binder classification tasks, while achieving a 50x speedup at inference time. FlashAffinity replaces the expensive protein structure prediction models with a simple protein-ligand docking model and the PairFormer-based affinity scoring module with a cheap EGNN architecture. In summary, this work bridges the gap between accuracy and efficiency, enabling ultra-fast virtual screening of massive chemical libraries.