supercomputing

supercomputing

supercomputing

Open or Closed Artificial Intelligence: How Science Suffers When Technology is in the Hands of Big Companies

Two of the Nobel Prize winners in Chemistry 2024 are employees of Google DeepMind, who caused significant unrest among their colleagues in May. Hassabis and Jumper announced in *Nature* the results of their AlphaFold 3 model, with applications in drug design; however, they published it in a closed manner, with reviewers not even having access to the system, which contradicts the basic principles of scientific publication. We risk having the transformative potential of AI controlled by big tech companies.

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Nobel Prize in Chemistry for Baker, Hassabis and Jumper for computational protein design and structure prediction

The Royal Swedish Academy of Sciences has awarded the Nobel Prize in Chemistry 2024 on the one hand to David Baker for computational protein design, which makes it possible to construct proteins with functions not present in nature. On the other hand, jointly to Demis Hassabis and John M. Jumper of Google DeepMind, for the development of AlphaFold2, which allows the structure of the 200 million known proteins to be predicted at high speed. 

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Reaction: Artificial protein designed to degrade microplastics

Based on a defense protein of the strawberry anemone, researchers from the Barcelona Supercomputing Center, CSIC and the Complutense University of Madrid have designed, through artificial intelligence and the use of supercomputers, an artificial protein capable of degrading PET micro and nanoplastics, such as those used in bottles. According to the authors, its efficiency is between 5 and 10 times higher than that of the proteins currently used and it works at room temperature. The results are published in the journal Nature Catalysis.

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Reaction: Meta publishes ESMFold, a model that predicts the structure of hundreds of millions of proteins

Meta has applied language modelling to predict the structure of a large collection of proteins. The model, called ESMFold, is being presented this week in the journal Science after being published on the bioRxiv preprint article server in December 2022. EMSFold is faster than similar models such as AlphaFold, developed by Google's DeepMind and EMBL's European Bioinformatics Institute. The sequences of more than 617 million proteins - of which more than a third are predicted with a high degree of confidence - are published in open access in the ESM Metagenomic Atlas.

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