AI turns its artistry to creating new human proteins

Last spring, an artificial intelligence lab called OpenAI unveiled technology that lets you create digital images simply by describing what you want to see. Called DALL-E, it sparked a wave of similar tools with names like Midjourney and Stable Diffusion. Promising to speed the work of digital artists, this new breed of AI captured the imagination of both the public and the pundits — and threatened to generate new levels of online disinformation.

Social media is now teeming with the surprisingly conceptual, in which shockingly detailed, often photorealistic images are generated by DALL-E and other tools. “Photo of a teddy bear riding a skateboard in Times Square.” “Cute corgi in a house made out of sushi.” “Jeflon Zuckergates.”

But when some scientists consider this technology, they see more than just a way of creating fake photos. They see a path to a new cancer treatment or a new flu vaccine or a new pill that helps you digest gluten.

Using many of the same techniques that underpin DALL-E and other art generators, these scientists are generating blueprints for new proteins — tiny biological mechanisms that can change the way our bodies behave.

Our bodies naturally produce about 20,000 proteins, which handle everything from digesting food to moving oxygen through the bloodstream. Now, researchers are working to create proteins that are not found in nature, hoping to improve our ability to fight disease and do things that our bodies cannot on their own.

David Baker, the director of the Institute for Protein Design at the University of Washington, has been working to build artisanal proteins for more than 30 years. By 2017, he and his team had shown this was possible. But they did not anticipate how the rise of new AI technologies would suddenly accelerate this work, shrinking the time needed to generate new blueprints from years down to weeks.

“What we need are new proteins that can solve modern-day problems, like cancer and viral pandemics,” Baker said. “We can’t wait for evolution.” He added, “Now, we can design these proteins much faster, and with much higher success rates, and create much more sophisticated molecules that can help solve these problems.”

Last year, Baker and his fellow researchers published a pair of papers in the journal Science describing how various AI techniques could accelerate protein design. But these papers have already been eclipsed by a newer one that draws on the techniques that drive tools like DALL-E, showing how new proteins can be generated from scratch much like digital photos.

“One of the most powerful things about this technology is that, like DALL-E, it does what you tell it to do,” said Nate Bennett, one of the researchers working in the University of Washington lab. “From a single prompt, it can generate an endless number of designs.”

To generate images, DALL-E relies on what AI researchers call a neural network, a mathematical system loosely modelled on the network of neurons in the brain. This is the same technology that recognises the commands you bark into your smartphone, enables self-driving cars to identify (and avoid) pedestrians and translates languages on services like Skype.

A neural network learns skills by analysing vast amounts of digital data. By pinpointing patterns in thousands of corgi photos, for instance, it can learn to recognize a corgi. With DALL-E, researchers built a neural network that looked for patterns as it analysed millions of digital images and the text captions that described what each of these images depicted. In this way, it learned to recognize the links between the images and the words.

When you describe an image for DALL-E, a neural network generates a set of key features that this image may include. One feature might be the curve of a teddy bear’s ear. Another might be the line at the edge of a skateboard. Then, a second neural network — called a diffusion model — generates the pixels needed to realize these features.

The diffusion model is trained on a series of images in which noise — imperfection — is gradually added to a photograph until it becomes a sea of random pixels. As it analyses these images, the model learns to run this process in reverse. When you feed it random pixels, it removes the noise, transforming these pixels into a coherent image.

At the University of Washington, other academic labs and new startups, researchers are using similar techniques in their effort to create new proteins.

– This article originally appeared in The New York Times