# Accurate structure prediction of biomolecular interactions with AlphaFold 3

> The introduction of AlphaFold 2<sup>1</sup> has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design<sup>2-6</sup>. Here we describe our AlphaFold 3 model with a substantially updated diffusion-based...

## Metadata
- Authors: Josh Abramson, Jonas Adler, Jack Dunger, Richard Evans, Tim Green, Alexander Pritzel, Olaf Ronneberger, Lindsay Willmore, Andrew J. Ballard, Joshua Bambrick, Sebastian W. Bodenstein, David A. Evans, Chia-Chun Hung, Michael O’Neill, David Reiman, Kathryn Tunyasuvunakool, Zachary Wu, Akvilė Žemgulytė, Eirini Arvaniti, Charles Beattie, Ottavia Bertolli, Alex Bridgland, Alexey V. Cherepanov, Miles Congreve, Alexander I. Cowen-Rivers, Andrew Cowie, Michael Figurnov, Fabian B. Fuchs, Hannah Gladman, Rishub Jain, Yousuf A. Khan, Caroline M. R. Low, Kuba Perlin, Anna Potapenko, Pascal Savy, Sukhdeep Singh, Adrian Stecuła, Ashok Thillaisundaram, Catherine Tong, Sergei Yakneen, Ellen D. Zhong, Michał Zieliński, Augustin Žídek, Victor Bapst, Pushmeet Kohli, Max Jaderberg, Demis Hassabis, John Jumper
- Journal: Nature
- Published: 2024-05-08
- DOI: https://doi.org/10.1038/s41586-024-07487-w
- Citations: 11,938
- Source: OpenAlex
- Access: Open Access

## Technology Hub
- Hub: Protein Structure Prediction
- Discipline: Biochemistry / AI
- Hub URL: https://science-database.com/technology/protein-structure
- Hub llms.txt: https://science-database.com/technology/protein-structure/llms.txt

## Abstract
The introduction of AlphaFold 2<sup>1</sup> has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design<sup>2-6</sup>. 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<sup>7,8</sup>. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.

## Links
- DOI: https://doi.org/10.1038/s41586-024-07487-w
- OpenAlex: https://openalex.org/W4396721167
- PDF: https://www.nature.com/articles/s41586-024-07487-w_reference.pdf
- JSON API: https://science-database.com/api/v1/technology/protein-structure

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Paper ID: oa-W4396721167 | Hub: protein-structure