With new funding, Atomic AI envisions RNA as the next frontier in drug discovery

 The biotech industry is experiencing a rush of AI-powered tools for many aspects of the complex drug discovery process. But one that has flown under the radar, increasingly thought to be key to certain diseases but woefully understudied, is RNA. With $35 million in new funding, Atomic AI aims to do for RNA what AlphaFold did for proteins, and find entirely new treatments in the process.

If you can still recall your high school biology, you probably remember RNA as sort of a middle man between DNA (long term information storage) and proteins (the machinery of cellular life at the molecular level). But like most things in nature, it doesn’t seem to be quite that simple, explained Atomic AI’s CEO and founder, Raphael Townshend.

“There’s this central dogma that DNA goes to RNA, which goes to proteins. But it’s emerged in recent years that it does much more than just encode information,” he said in an interview with TechCrunch. “If you look at the human genome, about 2% becomes protein at some point. But 80 percent becomes RNA. And it’s doing… who knows what? It’s vastly underexplored.”

Compared to DNA and proteins, little work has been done in this area. Academia has focused on other pieces of the puzzle and pharmaceuticals have, partly as a consequence of that, pursued proteins as the mechanisms for drugs. The result is a severe lack of knowledge and data on RNA structures.

But what Atomic AI posits is that RNA is functional and worth pursuing as a method of treatment. The secret is in the “non-coding” regions of RNA, which are like the header and footer on a document. They do protein-like work but aren’t proteins — and they’re not the only example.

You can think about RNA strands as beaded necklaces, much more string than bead. The string is “floppy” and more or less what its detractors think it is: an intermediary. But every once in a while you get a really interesting knot that seems unlikely to have formed by accident. As with proteins, if you can figure out their structure, that goes a long way towards understanding what they do and how they can be affected.

“The key is to find those beads, those structured bits. It’s high information content, it’s targetable, and it’s likely functional as well,” said Townshend. “It’s seen in drug discovery as a key new frontier.”

An interesting idea for a graduate thesis, perhaps (and it was for Townshend), but how can you build a business around it?

First, if the field is about to become more important, building out the methods for studying has a lot of value. Then, if you do build those methods, you can be first in line to use them. Atomic AI is doing both simultaneously.