Atomic AI: Unlocking RNA

Authors

Packy McCormick

Tags

Top Highlights

• Atomic AI is a perfect example of the kind of company we want to invest in at Not Boring Capital. It’s built on cutting edge science at the intersection of Bits and Atoms, has a path to becoming a $10B+ company, and as importantly, will bend the world’s trajectory upwards if it succeeds.

• Specifically, by unlocking the structure of RNA, Atomic AI could unlock the potential of RNA-targeting and RNA-based medicines, help cure previously incurable diseases, and save millions of lives.

• This binary battle between Bits and Atoms is not how we view the world at Not Boring.

• Atomic AI is building the foundation for AI-driven RNA drug discovery. It uses deep learning to predict the structure of RNA molecules in order to identify druggable targets.

• If Atomic AI is successful, it will unlock the potential of RNA-targeting and RNA-based medicine. That might mean cures for cancer, more efficient, effective, and safer vaccines, and the opportunity to save and improve millions of lives. Many of the diseases Atomic AI is going after are currently undruggable.

• The fact that DNA is a double helix with two separable strands gave direct clues into how information was replicated between cells in the body, and ultimately between parents and their offspring.

• The entire biotechnology sector—which now produces north of $500 billion in annual revenue in the U.S. alone—is a result of this paradigm shift.

• The broad success of structural biology as a discipline extends beyond the double helix and the advent of rational drug design. Over the years, many technologies rapidly improved. It became easier to produce proteins, crystallize them, and solve their structures.

• X-ray based techniques became much easier and more accurate, and computers got much faster. Entirely new technologies like electron cryogenic electron microscopy came on the scene. As researchers solved more and more structures, they poured them into the Worldwide Protein Data Bank (PBD).

• Even with improvements to measurement tools, experimentally solving protein structures is more of an art than a science.

• One of the major inflection points for the adoption of the technology was when a deep learning model dominated the ImageNet computer vision challenge in 2012, blowing other approaches out of the water.

• This challenge was a perfect opportunity because there was a massive amount of labeled training images, and incredibly clear metrics for success.

• After searching for problems with similar characteristics, Hassabis and DeepMind found the CASP protein challenge. It met all the criteria. There was a large amount of annotated protein data for training, and a clearly defined and challenging prediction problem.

• Being able to predict the structure of proteins is a  big deal for biotechnology. As we’ve seen, there is a deep connection between structure and function in biology. With these models, it’s now possible to explore the total space of protein structures on a scale that was previously unimaginable.

• Structure leads to function, and new understanding of structures leads to new bursts of productive activity.

• With protein structure prediction effectively solved, we are now seeing an explosion of academic and commercial efforts to design new protein-based drugs, vaccines, and biomaterials.

• With protein structure prediction effectively solved, we are now seeing an explosion of academic and commercial efforts to design new protein-based drugs, vaccines, and biomaterials.

• At its most fundamental level, I think biology can be thought of as an information processing system, albeit an extraordinarily complex and dynamic one. Taking this perspective implies there may be a common underlying structure between biology and information science - an isomorphic mapping between the two - hence the name of the company.

• Despite this enthusiasm, there were still important open questions to be addressed.

• The quality, centralized organization, and scale of data for protein structures is an anomaly in the life sciences.