The biotech industry is seeing a proliferation of AI-powered tools for many aspects of the complex drug discovery process. But one that flies under the radar, increasingly thought to be key to certain diseases, but terribly understudied, is RNA. His newly raised $35 million in funding, Atomic AI, aims to do to RNA what AlphaFold did to proteins, and find entirely new treatments in the process.
If you can remember high school biology, you probably remember RNA as something of a mediator between DNA (the long-term storage of information) and proteins (the machinery of cellular life at the molecular level). . But like most things in nature, it doesn’t seem that simple, explains Raphael Townshend, CEO and founder of Atomic AI.
“There is a central dogma that DNA becomes RNA and RNA becomes protein. said in “If you look at the human genome, at some point about 2% will be protein. But 80% will be RNA.
Compared to DNA and proteins, little research has been done in this area. Academia has turned its attention to other pieces of the puzzle, and the pharmaceutical industry, in part as a result, has pursued proteins as mechanisms of medicine. .
But Atomic AI argues that RNA is functional and worth pursuing as a therapeutic. The secret lies in the “non-coding” regions of RNA, such as document headers and footers. They act like proteins, but they are not proteins and are not the only examples.
An RNA strand can be thought of as a necklace of beads. It’s much more string than beads. The strings are “floppy” and more or less detractors think of it as a middleman. As with proteins, being able to figure out their structure goes a long way toward understanding what proteins do and how they are affected.
“The key is to find those beads, those bits that are structured. They’re informative, they’re highly targeted, and they’re probably also functional,” Townshend said. “It is seen as an important new frontier in drug discovery.”
Perhaps an interesting idea for a senior thesis (and it was for Townsend), but how do you build a business around it?
First, there is a lot of value in building study methods if the field becomes more and more important in the future. Then create these methods and you can use them first. Atomic AI is doing both at the same time.
At the core of Atomic’s IP, which is a simplification, is AlphaFold for RNA. The biology is different, the models work differently, but the idea is the same. A machine learning model trained on a limited set of molecules of one class can make accurate predictions about the structures of other molecules of that class.
Remarkably, Townshend’s team created a model that far outperformed others by giving structural signatures to just 18 RNA molecules “published between 1994 and 2006.” am. In 2021, this completely barebones model wiped the floor with other models, as he revealed in his front-page article published in Science.
Since then, Townshend quickly adds that the company has greatly enhanced its models and methods with more raw materials, many of which it makes on its own in its own wet lab. They call their updated set of tools PARSE (AI-Driven RNA Structure Discovery Platform).
“The Science paper was the first breakthrough, but it actually generated a lot of … structural adjacency data,” he explained. “The data associated with the structure, tens of millions of data points, rather than the full structure itself. The same scale of data required to train a large language model. We were able to dramatically improve both the speed and accuracy of
So Atomic AI is, at least publicly, the only company with a system that can take raw data of an RNA molecule and spew out a reasonably reliable estimate of its structure. This will be useful for anyone doing RNA research inside or outside medicine, and gene therapy and mRNA vaccines are definitely increasing this field.
With such a tool, you can do one of two things: Either license it as a “structure-as-a-service” platform or use it yourself, as Townshend puts it. Atomic chose the latter and is pursuing its own drug discovery program.
This approach is very different from many AI discovery processes out there. The general idea is that you have a protein, say something you want to inhibit its expression in the human body, but what you don’t have is a chemical that binds reliably and exclusively to that protein. cheaper if possible).
AI drug discovery efforts generate thousands, millions, or even billions of candidate molecules that might work, rank them, and wet labs start processing the list as quickly as possible. tend to. If we can find something that meets the above properties, we can manufacture new drugs or replace more expensive drugs on the market. But importantly, we find new binders to known proteins. We are competing for
“We’re not just finding binders, we’re finding things we can target in the first place. The interesting reason is, at the end of the day, these big pharma companies are more interested in new biology than new molecules.” Finding this new target, rather than increasing the number of molecules available for targeting, allows us to do things we couldn’t do before,” said Townshend.
Not only that, but some proteins have been found to be nearly drug-resistant for some reason, causing drug-resistant diseases. may enable treatment of
At present, Atomic AI compiles a list of specific cancers that result in pathological protein overproduction (thus a good option for preempting mechanisms) and neuronal cancers that may also benefit from upstream interventions. Focused on degenerative diseases.
Of course, all this work is very expensive and requires both extensive lab work and intensive data science. Fortunately, the company is led by Playground Global, with participation from 8VC, Factory HQ, Greylock, NotBoring, AME Cloud Ventures, and angels Nat Friedman, Doug Mohr, Neal Khosla, and Patrick Hsu, for a $35 million A raised rounds. (The company previously raised $7 million in a seed round.)
“People are picking every fruit that’s readily available in protein land,” Townsend said. “Now there’s a new biology to pursue.”
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