Title: Triangle Multiplication is All You Need for Biomolecular Structure Representations
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: structure prediction, cofolding, triangle multiplication

Abstract: 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.

TL;DR: Simple biomolecular structure prediction architecture