AI tools are designing entirely new proteins that could transform medicine

From the article:

[RFdiffusion and similar protein-designing AIs] are based on the same principles as neural networks that generate realistic images, such as Stable Diffusion, DALL-E and Midjourney. These ‘diffusion’ networks are trained on data, be they images or protein structures, which are then made progressively noisier, eventually bearing no resemblance to the starting image or structure. The network then learns to ‘denoise’ the data, performing the task in reverse.

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[...] Baker’s team conditioned RFdiffusion to make proteins that include a specific fold, or that can nestle against the surface of another molecule (an interaction that underlies binding). Grigoryan’s team even developed a diffusion network called Chroma and then conditioned it to make proteins shaped to resemble the 26 capital letters used in English, as well the Arabic numerals.

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Baker’s team is cranking out so many designs that testing whether they work as intended has become a serious bottleneck. “One machine-learning person can generate enough designs to keep 100 biologists busy for months,” says Kevin Yang, a biomedical machine-learning researcher at Microsoft Research in Cambridge, Massachusetts whose team has developed its own diffusion-based protein design tool.

But early signs suggest that RFdiffusion’s creations are the real deal. In another challenge described in their study, Baker’s team tasked the tool with designing proteins containing a key stretch of p53, a signalling molecule that is overactive in many cancers (and a sought-after drug target). When the researchers made 95 of the software’s designs (by engineering bacteria to express the proteins), more than half maintained p53’s ability to bind to its natural target, MDM2. The best designs did so around 1,000 times more strongly than did natural p53. When the researchers attempted this task with hallucination, the designs — although predicted to work — did not pan out in the test tube, says Watson.

Overall, Baker says his team has found that 10–20% of RFdiffusion’s designs bind to their intended target strongly enough to be useful, compared with less than 1% for earlier, pre-AI methods. (Previous machine-learning approaches were not able to reliably design binders, Watson says). Biochemist Matthias Gloegl, a colleague at UW, says that lately he has been hitting success rates approaching 50%, which means it can take just a week or two to come up with working designs, as opposed to months. “It’s really insane,” he says.