A chess-playing AI network solved a 50-year-old biological dilemma, potentially revolutionizing drug development
DeepMind's AI networks have spent the past few years destroying human players in chess, Go, and classic video games. Now, they're ready to help humans out.
On Monday, DeepMind revealed its AI system AlphaFold had cracked a 50-year-old biological challenge, accurately predicting how proteins within the human body fold into 3D shapes based on their DNA sequences. Those shapes are key in determining how a protein works, and in turn pivotal to figuring out how to treat diseases that involve those proteins, The Guardian explains.
Proteins, which are sequences of amino acids within living creatures, can "bend into a mind-boggling variety of shapes," The Guardian writes. It takes about a year and costs around $120,000 to identify a single protein's shape using the most common method, known as X-ray crystallography, Fortune reports. DeepMind had AlphaFold study 170,000 protein sequences and shapes that had already been identified, and after a few weeks, AlphaFold was ready to face off against other computer-based protein structure predictors in an international competition called CASP.
When asked to extrapolate 100 protein shapes from their amino acid sequences, AlphaFold beat out every other program in CASP and produced results that rivaled lab methods. It predicted a protein's structure within an atom's width of accuracy in two-thirds of those proteins, and was "highly accurate" in the other third, per Fortune. It also only took a few days to identify each protein.
CASP co-founder John Moult called AlphaFold's results a "big deal," telling Nature that "in some sense the problem is solved." If scientists can more quickly figure out a protein's shape, they can find out how it affects other cells — for example, discovering how COVID-19's spike proteins latch onto host cells helped scientists develop vaccines that reduce transmission. DeepMind CEO Demis Hassabis said the company is working on how to share AlphaFold with researchers, and that some scientists have already started using it on vexing protein analyses of their own.
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