AlphaFold predicted almost every protein known to science

AlphaFold predicted almost every protein known to science

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AlphaFold predicted almost all proteins known to science

DeepMind-min AlphaFold predicted almost every protein known to science

AlphaFold, an algorithm from DeepMind’s AI lab, has predicted the structures of more than 200 million proteins. This is almost all known to science compounds found in plants, bacteria and animals.

According to the developers, thanks to the open source AlphaFold scientists from around the world can use it in their research. In July 2021, the algorithm deciphered 350,000 3D structures. Since then, thousands of scientists have used the system, DeepMind said.

“More than 500,000 researchers and biologists have used the database to look at more than 2 million structures. And these predictive structures have helped scientists make brilliant new discoveries,” said DeepMind founder and CEO Demis Hassabis.

For example, in April 2022, scientists at Yale University used the AlphaFold database to develop a new, highly effective vaccine against malaria. In July 2021, researchers at the University of Portsmouth turned to the system in creating enzymes that will fight pollution from single-use plastic.

“This moved us forward a year, if not two,” said John McGeehan, director of the Portsmouth Center for Enzyme Innovation.

DeepMind also reported that in the past year, scientists have https://gagarin.news/news/ftx-bankruptcy-who-is-behind-it/ published more than 1,000 papers applying AlphaFold. In the future, the researchers plan to bring the algorithm to create drugs against poorly researched but widespread tropical diseases like leishmaniasis.

Recall that in July 2021, DeepMind introduced the AlphaFold algorithm, which simulated 20,000 human protein structures. Developers opened the program to researchers from around the world.

In November, Alphabet founded Isomorphic Laboratories, which will use artificial intelligence to find new types of drugs.

In July 2022, researchers at MIT developed EquiBind, a deep-learning model that is 1,200 times faster than its counterparts at linking molecules to proteins in drug development.

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