Chaitanya K. Joshi

Chaitanya K. Joshi

@chaitjo.bsky.social

AI researcher excited about biomolecule design 🧬 PhD student at the University of Cambridge Prev. at FAIR, Prescient Design, and MRC LMB 📝 https://chaitjo.substack.com

1.1K followers178 following131 posts

Excited to have Edric Choi present his latest work on RNA chemical mapping at the CASP Nucleic Acids Reading Group! Thursday April 16 2026 on Zoom; Pacific Daylight Saving Time 8 am / Eastern Daylight Saving Time 11 am -- free to attend and full of interactive discussions.

New blog 💙: I reflect on why I worked on what I worked on... I think a PhD is a very special time. You get to challenge yourself, push your boundaries, and grow. My thoughts go against the current AI/academia narrative online, so I hope you find it interesting. chaitjo.substack.com/p/phd-thesis...

chaitjo.substack.comA Cambridge PhD thesis in three research questionsGeometric Deep Learning for Molecular Modelling and Design: A personal scientific journey

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)

Happy new year! A step change in RNA structure prediction, powered by top Kaggle-ers in a collaboration lead by Stanford University and NVIDIA Happy to have played a small part in the new RNAPro model, significantly outperforming AlphaFold 3 as well as VFold (CASP winners)

Das Lab@rdaslab.bsky.social

Preprint on the Stanford #RNA 3D folding Kaggle challenge is out. My scientific new year’s resolution is to brush up on template-based modeling. #gofai www.biorxiv.org/content/10.6...

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.

I wrote some personal reflections about being physically embedded in a world-leading molecular biology lab @mrclmb.bsky.social, learning to communicate with experimentalists, back-breaking wet lab work, scientific rigour, and skin in the game! 💌: chaitjo.substack.com/p/an-ai-rese...

chaitjo.substack.comAn AI Researcher in the Cathedral of Molecular BiologyHow we designed catalytic RNA functions, and what I learned holding a pipette at the MRC Laboratory of Molecular Biology.

Excited to release the fully open-source code for gRNAde - our wet-lab validated, generative AI framework for 3D RNA inverse design 🚀⭐️ I pride myself on open-science & this is probably the most intense release I've done!

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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:

- 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...

github.comGitHub - chaitjo/geometric-rna-design: gRNAde is a Generative AI framework for inverse design of 3D RNA structure and functiongRNAde is a Generative AI framework for inverse design of 3D RNA structure and function - chaitjo/geometric-rna-design

Excited to release the fully open-source code for gRNAde - our wet-lab validated, generative AI framework for 3D RNA inverse design 🚀⭐️ I pride myself on open-science & this is probably the most intense release I've done!

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?

Chaitanya K. Joshi reposted

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 1/4

arxiv.orgLeMat-GenBench: A Unified Evaluation Framework for Crystal Generative ModelsGenerative 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...

Chaitanya K. Joshi reposted

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)

Chaitanya K. Joshi reposted

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. 😎

Chaitanya K. Joshi@chaitjo.bsky.social

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! 🧵👇

Chaitanya K. Joshi reposted

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! 🧬🧪

Chaitanya K. Joshi@chaitjo.bsky.social

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! 🧵👇

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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.

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 🏝️

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!

Chaitanya K. Joshi reposted

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

pmc.ncbi.nlm.nih.govKeeping RNA happyAn official website of the United States government
Chaitanya K. Joshi@chaitjo.bsky.social

🚀🧬 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...

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.

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.

Chaitanya K. Joshi reposted

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 🧵

Chaitanya K. Joshi reposted

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

Samuel King@samuelhking.bsky.social

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 🧵

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)

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 🤯

Chaitanya K. Joshi reposted

(1/7) 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...)

biorxiv.orgAccelerating Biomolecular Modeling with AtomWorks and RF3Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilita...

🧬 Protein designers: we keep saying we're getting really good at making static, rock-like proteins... Introducing Dynamic MPNN - our first step towards truly multi-state and 'programmable' protein design! Great collaboration led by Alex Abrudan and Sebastian Pujalte!

LLMs transfer knowledge across domains; eg. doing well on maths translates to getting better at programming, too. Molecular models often don't - We may need unified representations for true generalization.