The study claims that AI and computational methods have largely solved the long-standing problem of predicting protein structures from amino acid sequences and can also design entirely new proteins. This is because most monomeric protein structures can now be predicted with high accuracy, enabling databases containing hundreds of millions of predicted structures, while computational methods can generate protein structures not found in nature.[1]
[1] Scientific background: Nobel Prize in Chemistry 2024Article
Nobel Committee for Chemistry · 6 Oct 2026
Expert synthesis; not an independent validation of every prediction.
The successor model extends prediction to complexes with DNA, RNA and small molecules. Shows the field moving past single-protein structures, the part often described as solved.[2]
[2] Accurate structure prediction of biomolecular interactions with AlphaFold 3 - NatureAcademic Paper
Nature · 6 Oct 2026
Passage from the sourceAbstract The introduction of AlphaFold 2/1 has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design2,3,4,5,6. 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.2.37,8. Together, these results show that high-accuracy modelling across biomolecular space is possible within a single unified deep-learning framework.
Fold-switching proteins are a small minority of known proteins.