Triangle Multiplication Is All You Need For Biomolecular Structure Representations
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915652735860736 |
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| author | Ouyang-Zhang, Jeffrey Murugan, Pranav Diaz, Daniel J. Scarpellini, Gianluca Bowen, Richard Strong Gruver, Nate Klivans, Adam Krähenbühl, Philipp Faust, Aleksandra Al-Shedivat, Maruan |
| author_facet | Ouyang-Zhang, Jeffrey Murugan, Pranav Diaz, Daniel J. Scarpellini, Gianluca Bowen, Richard Strong Gruver, Nate Klivans, Adam Krähenbühl, Philipp Faust, Aleksandra Al-Shedivat, Maruan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18870 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Triangle Multiplication Is All You Need For Biomolecular Structure Representations Ouyang-Zhang, Jeffrey Murugan, Pranav Diaz, Daniel J. Scarpellini, Gianluca Bowen, Richard Strong Gruver, Nate Klivans, Adam Krähenbühl, Philipp Faust, Aleksandra Al-Shedivat, Maruan Quantitative Methods Machine Learning 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. |
| title | Triangle Multiplication Is All You Need For Biomolecular Structure Representations |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2510.18870 |