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Running AlphaFold at scale
By Joran Martijn, March 2026
A brief history of AlphaFold
The challenge of predicting protein structures from amino acid sequences has long been a significant issue in biochemistry, and many groups have taken a crack at it for several decades.
In 2016 Google's DeepMind began exploring protein folding as part of its research into using AI for biological problems. AlphaFold made its first notable appearance in the 13th Critical Assessment of Protein Structure Prediction (CASP13) in December 2018. It showcased its ability to predict protein structures with remarkable accuracy, outperforming other methods.
In 2020, DeepMind released AlphaFold2, which significantly improved upon the original model. It utilized new neural network architectures and training techniques, demonstrating astonishing accuracy in predictions. During CASP14, AlphaFold2 solved protein structures at a level comparable to experimental techniques.
In July 2021, DeepMind published the AlphaFold Protein Structure Database, providing over 350,000 predicted protein structures for the scientific community. This database has since been a vital resource for researchers worldwide.
AlphaFold3 was released in 2024. It's main development relative to its predecessor was that it can predict structures of protein complexes with non-protein molecules (DNA, RNA, ions, lipids, Fe-S clusters, small molecule ligands, post-transcriptional modifications, chemical modifications of nucleic acids). It can also predict structures without proteins, such as ss- and dsDNA and ssRNA chains.