{"ID":2852391,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.18870","arxiv_id":"2510.18870","title":"Triangle Multiplication Is All You Need For Biomolecular Structure Representations","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. Code is available at https://github.com/genesistherapeutics/pairmixer.","short_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-...","url_abs":"https://arxiv.org/abs/2510.18870","url_pdf":"https://arxiv.org/pdf/2510.18870v2","authors":"[\"Jeffrey Ouyang-Zhang\",\"Pranav Murugan\",\"Daniel J. Diaz\",\"Gianluca Scarpellini\",\"Richard Strong Bowen\",\"Nate Gruver\",\"Adam Klivans\",\"Philipp Krähenbühl\",\"Aleksandra Faust\",\"Maruan Al-Shedivat\"]","published":"2025-10-21T17:59:02Z","proceeding":"q-bio.QM","tasks":"[\"q-bio.QM\",\"cs.LG\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false,"code_links":[{"ID":607995,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2852391,"paper_url":"https://arxiv.org/abs/2510.18870","paper_title":"Triangle Multiplication Is All You Need For Biomolecular Structure Representations","repo_url":"https://github.com/genesistherapeutics/pairmixer","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}