mRNA

mRNA

The breadth of disease-causing proteins is a fertile hunting ground for drug research. Many biotech startups are trying to find small molecules capable of binding to these targets. That’s great when it works, but many protein targets remain elusive, said Raphael Townsend, founder and CEO of Atomic AI. Instead of chasing disease-causing proteins, Townsend’s startup is aiming for another target: the RNA that carries the instructions for making those proteins. Atomic AI uses artificial intelligence to find ways to anesthetize RNA and is now out of stealth, backed by $35 million.

“By targeting RNA instead, you give yourself new ways to target these incurable diseases,” Townsend said.

The Series A funding round announced Wednesday, was led by Playground Global.

To treat RNA, scientists must first understand it better. Proteins are relatively well understood, with hundreds of thousands of known protein structures, Townsend said. In comparison, the human transcriptome, the complete set of all RNAs, is poorly understood. The hundreds of known RNA structures are less well defined than proteins, Townsend said. This is key because there is growing recognition that RNA itself plays a major role in disease, he added.

Proteins fold and change shape, which can make them difficult to hit with a small molecule. But RNA is significantly more flexible, making it more of a moving target, Townsend said. San Francisco-based Atomic AI’s technology maps the transcriptome with an approach that combines wet-lab experiments with computational analysis. Data generated by the wet lab is used to train AI to discover new targets in the three-dimensional structure of RNA, Townsend said. AI makes predictions that inform further wet lab experiments. These results feed additional AI analysis, continuing a virtuous cycle.

Atomic AI’s technology is based on research stemming from Townsend’s doctoral work at Stanford University. This research was published in the journal Science in 2021, the same year that Atomic AI was formed. Since then, the company has made progress with its algorithms and wet lab, Townsend said. The technology, now called Platform for AI-driven RNA Structure Exploration (PARSE), has also improved in speed and accuracy.

The new capital allows Atomic AI to scale the platform, allowing the startup to become a drug discovery organization, Townsend said. The company will begin to narrow down the targets it will pursue. Townsend declined to identify specific diseases Atomic AI might pursue, but said the technology could be used to discover small molecules for use in oncology, neurodegenerative diseases, cardiology, rare diseases and infectious diseases. The startup’s initial research will focus on identifying the parts of the transcriptome that can even be targeted, Townsend said.

Atomic AI isn’t the first biotech to look to use RNA, and in addition to having an earlier head start, some of these startups already have partnerships with big pharmaceutical companies. The most modern program of Arrakis Therapeutics is an oncology lead optimization compound. The Waltham, Massachusetts-based company has a drug discovery alliance with Roche. Skyhawk Therapeutics is another Waltham-based company developing small molecules that target RNA. This company has alliances with Bristol Myers Squibb, Merck and Takeda Pharmaceutical. Instead of targeting RNA directly, Remix Therapeutics is developing drugs that target parts of the cell that process it. Almost a year ago based in Cambridge, Massachusetts Remix has entered into a research alliance with a subsidiary of Johnson & Johnson. Most recently based in Boulder, Colorado Arpeggio Biosciences has disclosed a $17 million Series A funding round.

Townsend acknowledges the other companies pursuing RNA-targeted small molecules, but says what sets Atomic AI apart is the wet-lab component of its platform. Companies that take an all-AI approach to RNA will struggle because there simply isn’t a lot of RNA data for these technologies to analyze, he explained.

Now that Atomic AI is out of stealth, Townsend said he’s looking for potential partnerships. While the startup’s internal research will focus on small-molecule drug development, Townsend said the partnerships will focus on using PARSE to develop new RNA-based drugs. The platform’s ability to predict how RNA folds and forms new structures can be used to design new RNA drugs, he explained. The technology also has the potential to improve certain aspects of RNA-based drugs, such as stability. For example, a more stable RNA molecule could avoid the ultra-cold storage required by messenger RNA-based Covid-19 vaccines.

Atomic AI initially raised $7 million in seed funding in 2021, led by 8VC. That firm also invested in a Series A round that included participation from Factory HQ; Greylock; It’s not boring; AME Cloud Ventures; and angel investors, including former GitHub CEO Nat Friedman; Doug Mohr; Curai CEO Neal Khosla; and Patrick Hsu, UC Berkeley professor and co-founder of the Arc Institute.

Image by libre de droit, Getty Images

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