Chaitanya K. Joshi

LeMat-GenBench: A Unified Evaluation Framework for Crystal Generative Models Generative machine learning (ML) models hold great promise for accelerating materials discovery through the inverse design of inorganic crystals, enabling an unprecedented exploration of chemical spac...

Happy to have contributed to and now finally share LeMat-GenBench, a new open benchmark + leaderboard for generative crystalline materials models! ⚛️✨

It provides standardised metrics for validity, stability, & much more. Already includes results for 12 models!

🔗 Paper: arxiv.org/abs/2512.04562
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Thank you to everyone who made the inaugural Virtual Cell Challenge a success.

Over 5,000 participants from 114 countries competed to build AI models that predict cellular responses to genetic perturbations. Today we're announcing the winners and reflecting on what we learned.

I think the term ‘Virtual cell’ will have the same trajectory as ‘AGI’ or ‘Foundation models’: Initially opposed by rigorous scientists, while the Bay Area and Demis Hassabis are the only ones comfortable using it

→ becoming a mainstream term in academia soon, in few years (Overton window)

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

An AI researcher interested in biochemistry modeling successfully improved his RNA language model through participation in the Eterna pseudoknot design competition. Congratulations, Chaitanya! 🧬🧪 #RNAsky

The polymerase ribozyme results are pretty cool too. 😎

Enumerating possible pseudoknots that a sequence can form with nearest-neighbor models is an NP-hard problem. Even evaluating these structures is challenging, let alone designing them. So it’s great to see data-based models starting to crack the RNA structural design problem! 🧬🧪

An AlphaGo moment for RNA design How our AI system, gRNAde, matched human experts at the complex game of RNA folding—and why it matters.

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...

Generative inverse design of RNA structure and function with gRNAde The design of RNA molecules with bespoke three-dimensional structures and functions is a central goal in synthetic biology and biotechnology. However, progress has been limited by the challenges of de...

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!

Introducing gRNAde: our own little "AlphaGo Moment" for RNA design! 🧬🚀

📝: tinyurl.com/gRNAde-paper

Unlike proteins, RNA design has long relied on "wisdom of the crowd" (human experts) or the slow crawl of directed evolution — gRNAde changes that! 🧵👇

Wow thank you, Jamie, for sharing! 🤗

To make future progress it’s worth revisiting the past. From Olke Uhlenbeck, “Keeping RNA Happy”
pmc.ncbi.nlm.nih.gov/articles/PMC...

Beyond structure-based biomolecule design Dynamics, black-box data, and the antedisciplinary frontier of biomolecule design

🚀🧬 Beyond Structure-based Biomolecule Design

Its an important moment for structure-based biomolecule design: models starting to work and action shifting from academia to industry.

So what are the next scientific problems academia could be thinking about?

chaitjo.substack.com/p/beyond-str...

And at big industrial labs with large budgets, the scientists are just able to do a lot more intuition-building. They are able to try ideas a lot faster, and get a 'feel' for what ideas will work much faster as a result.

Why do 'frontier' labs train the best models?

I think its because training deep learning models is less like science/engineering, and more like cooking. It takes some time to develop the intuitions around learning dynamics of big models.

The results are in: top codes in Stanford #RNA 3D Folding @kaggle.com competition are competitive with CASP16-leading humans Vfold, beat AlphaFold 3. Top team’s trick was template-based modeling, not #DeepLearning. Congrats: john, odat, Eigen, + all 1706 participants! www.kaggle.com/competitions...

🚨To accommodate the addition of EuroMLSB, we have extended the submission deadline to October 1, 2025 11:59pm AoE.

Find information on paper guidelines at mlsb.io. Submissions will be made through CMT.

Genome language models can generate new, high-fitness bacteriophages!

@samuelhking.bsky.social @claudiadriscoll.bsky.social
@david-li.bsky.social @danguo.bsky.social @adititm.bsky.social Garyk Brixi @maxewilkinson.bsky.social @brianhie.bsky.social

www.biorxiv.org/content/10.1...

Many of the most complex and useful functions in biology emerge at the scale of whole genomes.

Today, we share our preprint “Generative design of novel bacteriophages with genome language models”, where we validate the first, functional AI-generated genomes 🧵

very cool work and a milestone in synthetic biology. how impressive are the new phage genomes?

with generative bioML, i'm always looking at how similar the generated sequences are to known sequences. let's take a look

You asked and we listened... @workshopmlsb.bsky.social is excited to be expanding to Copenhagen, DK at @euripsconf.bsky.social 🎉

Two workshops (San Diego & Copenhagen) will run concurrently to support broader attendance. You can indicate your location preference(s) in the submission portal💫

The 3D structure of biomolecules are nature's 'thinking tokens' enroute to the output that we actually truly want to understand: Function.

(Slide from Denny Zhou's Stanford talk on LLM Reasoning)

We have restarted our global Nucleic Acid Strcuture webinar series to bring the expiremental and computational communities together to discuss new developments in the field. Join us this Thursday for the next webinar. Sign up to our mailing list here: groups.google.com/g/casp-rna-sig

Scaling laws for BioML and wet lab data will eventually work out in the right setting! After all, language data for LLMs was acquired by the largest wet lab experiment ever conducted: Human civilisation 🤯

Accelerating Biomolecular Modeling with AtomWorks and RF3 Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilita...

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Training biomolecular foundation models shouldn't be so hard. And open-source structure prediction is important. So today we're releasing two software packages: AtomWorks and RosettaFold3 (RF3)

[https://www.biorxiv.org/content/10.1101/2025.08.14.670328v2](www.biorxiv.org/content/10.1...)

It can train foundation models, but can it train frontier models too? ;)

📢 Submissions for MLSB 2025 are officially open! We invite researchers to submit their work on the intersection of AI and structural biology.

🗓️ Deadline: September 26, 2025 🔗 More info: cmt3.research.microsoft.com/MLSB2025

RosettaFold 3 is here! 🧬🚀

AtomWorks (the foundational data pipeline powering it) is perhaps the really most exciting part of this release!

Congratulations @simonmathis.bsky.social and team!!! ❤️

bioRxiv preprint: www.biorxiv.org/content/10.1...