Facebook, as the company was known before changing its name, has always been seen as a leader in AI. The popular open-source framework PyTorch was Facebook’s creation and earlier this year Meta became a founding member of a foundation aiming to drive the adoption of AI.

In its pursuit to become a leader in the metaverse, changing its very company name to reflect, many people – including shareholders – have been concerned that it will reduce its focus on other important areas.

Brad Gerstner, the founder of Meta shareholder Altimeter Capital, penned a letter in which he urged Meta to reduce its metaverse investments and “solidify the company’s position” as one of the world’s leaders in AI.

“Meta’s investment in AI will lead to exciting and important new products that can be cross-sold to billions of customers. From Grand Teton to Universal Speech Translator to Make-A-Video, we are witnessing a Cambrian moment in AI, and Meta is no doubt well positioned to help invent and monetize that future,” wrote Gerstner.

“Perhaps it was the re-naming of the company to Meta that caused the world to conclude that you were spending 100% of your time on Reality Labs instead of AI or the core business. Whatever the reason, that is certainly the perception.”

Meta’s announcement this week of its protein-folding AI could help to alleviate such concerns.

The company has released the ESM Metagenomic Atlas – which features over 600 million proteins and predictions for almost the entire MGnify90 database – in addition to the model used to create the database and an API that allows researchers to use it for scientific discovery.

Meta says that it found using a language model of protein sequences accelerated structure prediction by up to 60x.

“ESMFold shows how AI can give us new tools to understand the natural world, much like the microscope, which enabled us to see into the world at an infinitesimal scale and opened up a whole new understanding of life,” explained Meta. 

“Much of AI research has focused on helping computers understand the world in a way similar to how humans do. The language of proteins is one that is beyond human comprehension and has eluded even the most powerful computational tools. AI has the potential to open up this language to our understanding.”

ESM code and models can be found on GitHub here.

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Last year, the organisers of the biennial Critical Assessment of protein Structure Prediction (CASP) recognised AlphaFold as a solution to the grand challenge of figuring out what shapes proteins fold into.

Professor John Moult, Co-founder and Chair of CASP, University of Maryland, said:

“We have been stuck on this one problem – how do proteins fold up – for nearly 50 years.

To see DeepMind produce a solution for this, having worked personally on this problem for so long and after so many stops and starts, wondering if we’d ever get there, is a very special moment.”

AlphaFold is a major scientific advance that will play a crucial role in helping scientists to solve important problems such as the protein misfolding associated with diseases such as Alzheimer’s, Parkinson’s, cystic fibrosis and Huntington’s disease.

Arthur D. Levinson, Founder and CEO of Calico, explained:

“AlphaFold is a once-in-a-generation advance, predicting protein structures with incredible speed and precision.

This leap forward demonstrates how computational methods are poised to transform research in biology and hold much promise for accelerating the drug discovery process.”

Using AI, AlphaFold has successfully predicted the structure of nearly all 20,000 proteins expressed by humans. An independent benchmark proved the system was capable of predicting the shape of a protein to a decent standard around 95 percent of the time.

DeepMind is now releasing its database of every single protein in the human body, as well as for the proteins of 20 additional organisms that scientists rely on for their research, for free, for any researchers to use for the betterment of humankind.

“This will be one of the most important datasets since the mapping of the Human Genome,” said Ewan Birney, Deputy Director-General of EMBL and Director of EMBL-EBI.

Thanks to the “astonishingly accurate” models produced by AlphaFold, Professor Andrei Lupas, Director of the Max Planck Institute for Developmental Biology, claims they were able to solve a protein structure they were stuck on for close to a decade.

The full AlphaFold protein structure database can be accessed for free online here.

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Conditions such as Alzheimer’s, Parkinson’s, Huntington’s, and cystic fibrosis are suspected to be caused by misfolded proteins. Being able to predict a protein’s shape enables a greater understanding of its role within the body.

Previous techniques used for determining the shapes of proteins – such as cryo-electron microscopy, nuclear magnetic resonance, and X-ray crystallography – takes years and costs tens of thousands of dollars per structure.

AI, the researchers hope, will enable target shapes to be modelled from scratch without requiring previously solved proteins to be used as templates.

DeepMind calls their AI-powered folding efforts AlphaFold.

AlphaFold uses two different methods to construct predictions of protein structures:

1. 1. The first method repeatedly replaces pieces of a protein structure with new protein fragments, building on a technique commonly used in structural biology. A neural network invents new fragments.

1. The second method is called ‘gradient descent’ which is a mathematical technique applied to entire protein chains rather than pieces and makes small, incremental improvements.

Image Credit: DeepMind

DeepMind says its work is a successful demonstration of how AI can reduce the complexity of tasks such as protein folding; speeding up the diagnosis and treatment of some of the world’s most debilitating conditions.

In a contest organised by the Protein Structure Prediction Centre, AlphaMind was judged the winner among a total 98 algorithms by predicting the shapes of 25 out of 43 proteins. The runner-up, in comparison, could only predict three of the 43 proteins.

“For us, this is a really key moment,” said Demis Hassabis, co-founder and CEO of DeepMind. “This is a lighthouse project, our first major investment in terms of people and resources into a fundamental, very important, real-world scientific problem.”

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