AlphaFold has transformed protein structure prediction, but emerging applications such as virtual ligand screening, proteome-wide folding, and de novo binder design demand predictions at a massive scale, where runtime and memory costs become prohibitive. A major bottleneck lies in the Pairformer backbone of AlphaFold3-style models, which relies on computationally expensive triangular primitives-especially triangle attention-for pairwise reasoning. We introduce Pairmixer, a streamlined alternative that eliminates triangle attention while preserving higher-order geometric reasoning capabilities that are critical for structure prediction. Pairmixer substantially improves computational efficiency, matching state-of-the-art structure predictors across folding and docking benchmarks, delivering up to 4x faster inference on long sequences while reducing training cost by 34%. Its efficiency alleviates the computational burden of downstream applications such as modeling large protein complexes, high-throughput ligand and binder screening, and hallucination-based design. Within BoltzDesign, for example, Pairmixer delivers over 2x faster sampling and scales to sequences ~30% longer than the memory limits of Pairformer. Code is available at https://github.com/genesistherapeutics/pairmixer.
@article{arxiv.2510.18870,
title = {Triangle Multiplication Is All You Need For Biomolecular Structure Representations},
author = {Jeffrey Ouyang-Zhang and Pranav Murugan and Daniel J. Diaz and Gianluca Scarpellini and Richard Strong Bowen and Nate Gruver and Adam Klivans and Philipp Krähenbühl and Aleksandra Faust and Maruan Al-Shedivat},
journal= {arXiv preprint arXiv:2510.18870},
year = {2025}
}