Four years of research on Geometric Deep Learning & Molecular Design, summarized in 3 questions:

- How expressive are our models? (Theory)

- Can we build foundation models for molecules? (Architecture)

- Can AI design RNA in the real world? (Application)

It relies on new algorithms for template search, invented by Kagglers such as G. John Rao. If no templates are found, the model can still work and works decently well without templates, too.

The main takeaway from the Kaggle competition was how powerful RNA template modeling can be — it was kind of shocking to us 🤯

RNAPro integrates 3D templates into Protenix/the AF3 architecture, and this moves the needle on automated RNA structure prediction!

The video was largely created using AI text-to-video, and I must say the results are pretty impressive! There's huge potential for this technology to make science more approachable, just like Google's NotebookLM.

- How to set up the design prompt just right?
- How many sequences to sample and how to make them diverse?
- How exactly is filtering done step-by-step?
- How to analyze wet-lab results and create figures?

You can find out all of that in our GitHub codebase!

💻: github.com/chaitjo/geom...

With this release, we are fully openly releasing all the tacit knowledge for getting biomolecule design models to actually work in wet labs!

Seemingly small-but-critical details are often missing in flagship papers, and it took us a while to finetune our pipeline:

Many papers have built their RNA design pipelines using the gRNAde code & data from our ICLR 2025 spotlight paper - that's been exciting to see! Especially the attempt at biologically meaningful benchmarks 🤗

But how to take ML conference papers to wet-lab validated results?

Thankful for the tireless work and motivation from yourself and the Eterna team!

5/ Want the story behind the science? 🏰

I wrote a blog post about our "AlphaGo Moment" and what it was like being an AI researcher embedded at the legendary @mrclmb.bsky.social (holding a pipette for the first time!)

Read it on Substack: chaitjo.substack.com/p/alphago-mo...

4/ This was a massive team effort bridging AI and biology, from one end of Cambridge to another 🤗🚲

Thanks to Edo Gianni* @edogia.bsky.social, Sam Kwok*, @simonmathis.bsky.social, Pietro Liò, and @philholliger.bsky.social for this journey!

📄 Preprint: tinyurl.com/gRNAde-paper

3/ The Mechanistic Insight 👽

What did gRNAde learn? Humans stick close to nature, but gRNAde changes ~70% vs. WT sequence.

And it "sees" invisible 3D constraints that rational methods miss, allowing us to make "generative jumps" to new functional islands in sequence space 🏝️

2/ The Function Challenge ⚙️

But can we move from static shape to dynamic RNA machines?

We show how with a complex RNA Polymerase Ribozyme.

Rational design failed (3% success). gRNAde succeeded (31.5% active), discovering improved variants 15-20 mutations away from nature.

1/ The Structure Challenge 🧩

We entered gRNAde into Eterna OpenKnot: a CASP-style blinded, wet-lab competition by @eternagame.org

The result? Parity with the world's best humans, and big gains over Rosetta, RFdiffusion.

We can automate expert intuition for complex RNA folding!