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Demis Hassabis, maj 2024 (Jeff Chiu / AP)

Googleforskaren om att få priset: Jag blev liksom tom

En av årets tre Nobelpristagare i kemi, Demis Hassabis, säger till TT att han blev ”chockad” av beskedet.

– Det är väldigt surrealistiskt, det är svårt att greppa. Jag blev liksom tom i några minuter, säger han till TT.

Hassabis är en av forskarna på Googles Deepmind-avdelning där han arbetar han med att bygga system som kan förstå data och hitta strukturer i den.

Han har försökt att göra verklighet av en 50 år gammal teori om att man borde kunna förutse 3D-strukturer av protein – en av livets byggstenar – utifrån aminosyrasekvenser. För nyhetsbyrån beskriver han processen som ”utmanande beräkningsbiologi”.

Hassabis får tillsammans med kollegan John Jumper ena halvan av priset för sin prediktion av proteinstrukturer med hjälp av AI-modellen Alphafold. Andra halvan tillfaller David Baker, för hans arbete med databaserad proteindesign.

bakgrund
 
AlphaFold
Wikipedia (en)
AlphaFold is an artificial intelligence (AI) program developed by DeepMind, a subsidiary of Alphabet, which performs predictions of protein structure. The program is designed as a deep learning system. 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. The team achieved a level of accuracy much higher than any other group. 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. AlphaFold 2's results at CASP14 were described as "astounding" and "transformational". 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. Nevertheless, there has been widespread respect for the technical achievement. 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. The paper has since been cited more than 27 thousand times. AlphaFold 3 was announced on 8 May 2024. It can predict the structure of complexes created by proteins with DNA, RNA, various ligands, and ions.
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