The introduction of AlphaFold 2¹ has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design^2–6. In this paper, we describe our AlphaFold 3 model with a substantially updated diffusion-based architecture, which is capable of joint structure prediction of complexes including proteins, nucleic acids, small molecules, ions, and modified residues. The new AlphaFold model demonstrates significantly improved accuracy over many previous specialised tools: far greater accuracy on protein-ligand interactions than state of the art docking tools, much higher accuracy on protein-nucleic acid interactions than nucleic-acid-specific predictors, and significantly higher antibody-antigen prediction accuracy than AlphaFold-Multimer v2.3^7,8. Together these results show that high accuracy modelling across biomolecular space is possible within a single unified deep learning framework.

^{AlphaFold 2 1}的推出引发了蛋白质结构建模及其相互作用的一场革命，在蛋白质建模和设计中实现了广泛的应用^2-6。在本文中，我们描述了具有大幅更新的基于扩散的架构的 AlphaFold 3 模型，该模型能够对包括蛋白质、核酸、小分子、离子和修饰残基在内的复合物进行联合结构预测。新的 AlphaFold 模型比许多以前的专用工具显着提高了准确性：蛋白质-配体相互作用的准确性比最先进的对接工具高得多，蛋白质-核酸相互作用的准确性比核酸特异性预测器高得多，并且显着更高抗体-抗原预测准确性高于 AlphaFold-Multimer v2.3 ^7,8。这些结果共同表明，在单个统一的深度学习框架内可以实现跨生物分子空间的高精度建模。