AlphaFold

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AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure.^[1] The program is designed as a deep learning system.^[2]

AlphaFold software has had three major versions. A team of researchers that used AlphaFold 1 (2018) placed first in the overall rankings of the 13th Critical Assessment of Structure Prediction (CASP) in December 2018. The program was particularly successful at predicting the most accurate structure for targets rated as the most difficult by the competition organisers, where no existing template structures were available from proteins with a partially similar sequence. A team that used AlphaFold 2 (2020) repeated the placement in the CASP14 competition in November 2020.^[3] The team achieved a level of accuracy much higher than any other group.^[2][4] It scored above 90 for around two-thirds of the proteins in CASP's global distance test (GDT), a test that measures the degree to which a computational program predicted structure is similar to the lab experiment determined structure, with 100 being a complete match, within the distance cutoff used for calculating GDT.^[2][5]

AlphaFold 2's results at CASP14 were described as "astounding"^[6] and "transformational".^[7] Some researchers noted that the accuracy is not high enough for a third of its predictions, and that it does not reveal the mechanism or rules of protein folding for the protein folding problem to be considered solved.^[8][9] Nevertheless, there has been widespread respect for the technical achievement, and analysis suggests that AlphaFold 2 is accurate enough to predict even single-mutation effects.^[10] On 15 July 2021 the AlphaFold 2 paper was published in Nature as an advance access publication alongside open source software and a searchable database of species proteomes.^[11][12][13] Alternatives to AlphaFold have emerged, such as ESMFold, OmegaFold, RoseTTAFold, IntFOLD, and RaptorX.^[14] AlphaFold 3 was announced on 8 May 2024. Besides the improved accuracy on protein folding, it can also predict the structure and interactions of DNA, RNA, and of some ligands and ions.^[15]