AlphaFold 3
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AlphaFold 3 is a significant advancement in protein structure prediction using diffusion-based architecture for complex biomolecular interactions, relevant to AI safety as a major capability development in AI-driven biological research with potential dual-use implications.
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AlphaFold 3 introduces a substantially updated diffusion-based architecture capable of predicting the joint structure of complex biomolecular interactions including proteins, nucleic acids, small molecules, ions, and modified residues within a single unified framework. The model demonstrates significantly improved accuracy compared to specialized tools across multiple domains: superior performance for protein-ligand interactions versus state-of-the-art docking tools, higher accuracy for protein-nucleic acid interactions compared to nucleic-acid-specific predictors, and substantially better antibody-antigen prediction accuracy than AlphaFold-Multimer v.2.3. This work demonstrates that high-accuracy modeling across diverse biomolecular space is achievable through a single deep-learning framework rather than requiring separate specialized tools.
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| Page | Type | Quality |
|---|---|---|
| Scientific Research Capabilities | Capability | 68.0 |
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Accurate structure prediction of biomolecular interactions with AlphaFold 3
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An [Addendum](https://doi.org/10.1038/s41586-024-08416-7) to this article was published on 27 November 2024
## Abstract
The introduction of AlphaFold 2[1](https://www.nature.com/articles/s41586-024-07487-w#ref-CR1 "Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).") has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design[2](https://www.nature.com/articles/s41586-024-07487-w#ref-CR2 "Kreitz, J. et al. Programmable protein delivery with a bacterial contractile injection system. Nature 616, 357–364 (2023)."), [3](https://www.nature.com/articles/s41586-024-07487-w#ref-CR3 "Lim, Y. et al. In silico protein interaction screening uncovers DONSON’s role in replication initiation. Science 381, eadi3448 (2023)."), [4](https://www.nature.com/articles/s41586-024-07487-w#ref-CR4 "Mosalaganti, S. et al. AI-based structure prediction empowers integrative structural analysis of human nuclear pores. Science 376, eabm9506 (2022)."), [5](https://www.nature.com/articles/s41586-024-07487-w#ref-CR5 "Anand, N. & Achim, T. Protein structure and sequence generation with equivariant denoising diffusion probabilistic models. Preprint at arXiv https://doi.org/10.48550/arXiv.2205.15019 (2022)."), [6](https://www.nature.com/articles/s41586-024-07487-w#ref-CR6 "Yang, Z., Zeng, X., Zhao, Y. & Chen, R. AlphaFold2 and its applications in the fields of biology and medicine. Signal Transduct. Target. Ther. 8, 115 (2023)."). Here we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture that is capable of predicting the joint structure of complexes including proteins, nucleic acids, small molecules, ions and modified residues. The new AlphaFold model demonstrates substantially improved accuracy over many previous specialized tools: far greater accuracy for protein–ligand interactions compared with state-of-the-art docking tools, much higher accuracy for protein–nucleic acid interactions compared with nucleic-acid-specific predictors and substantially higher antibody–antigen prediction accuracy compared with AlphaFold-Multimer v.2.3[7](https://www.nature.com/articles/s41586-024-07487-w#ref-CR7 "Evans, R. et al
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