Kieran Didi

Our wet-lab validation campaign for Proteina-Complexa is now on bioRxiv! It includes some new exciting experimental results, from large protein structures (fully codesigned!) to functional carbohydrate binders. biorxiv.org/content/10.6... Thread with some of the additions 🧵1/n

It has also been exciting to see Protein-Complexa used in emerging agentic biology workflows—from work at Anthropic and Muni Bio to Levin Harness and the BioNeMo Agent Toolkit (and many others!) SOTA open models can become infrastructure for accelerating science! 4/n

Our wet-lab validation campaign for Proteina-Complexa is now on bioRxiv! It includes some new exciting experimental results, from large protein structures (fully codesigned!) to functional carbohydrate binders. biorxiv.org/content/10.6... Thread with some of the additions 🧵1/n

Sampling temperature gives us a useful dial: low temperature produces stable codesigns, higher temperature yields native-like structures. With collaborators in Munich/Leipzig, we pushed this to an 800-aa codesign—and obtained a crystal structure that closely matches the design! 2/n

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

That's it from me! Happy to hear what people think once they try it out (code weights etc all available under permissive license!) 19/19

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

Case study 5/5: first fully de novo Carbohydrate binders We target blood-group B antigen and got a a 21% hit rate. Massive shoutout to my long-term enzyme collaborator @mattpenner.bsky.social at Cambridge for powering this through, excited about what codesign will unlock for enzymes! 17/n

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

Case study 3/5: Targeting Kinases (PAK1 + CK1δ) with peptide binders Across mini-protein and short-peptide regimes (<31 aa peptides and 49–74 aa mini-binders), we observed strong hit rates on difficult target sites. 15/n

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

Case study 1/5: PDGFR We achieved a 63.5% hit rate, with top binders reaching double-digit picomolar affinity (best reported at 93.6 pM). Very cool collab with @novonordisk.bsky.social 13/n

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

Massive campaign: ~1M designed candidates screened across 127 diverse/challenging targets with all-to-all binding readouts. We solve more than 2/3 of the targets given a very limited compute budget and often quite challenging hotspots and crops 11/n

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

Quantitatively, these inference-time scaling strategies outperform prior hallucination-based methods under normalized compute budgets as well as other generative models, setting a new SOTA in in-silico binder design. 9/n

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

Data was also key: with @martinsteinegger.bsky.social and @sooyoung-cha.bsky.social lab we introduce Teddymer, a large synthetic binder-target pretraining resource built from domain-domain interactions derived from AFDB monomers (details + links on the project page). 7/n

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

We then combine the strengths of generative models with optimisation methods like hallucination. We call the inference recipe latent generative search: combine a learned generative prior with search at test time to steer toward better binders. 5/n

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

Proteina-Complexa is a generative binder design framework that supports diverse targets: single-chain proteins, multi-chain complexes, and small-molecule binding contexts. 3/n

📢 We’re launching Proteina-Complexa — and after the Jensen keynote mention, we definitely had to post this thread now ;) Atomistic binder design with generative pretraining + test-time compute, plus large-scale wet-lab validation. Project page: research.nvidia.com/labs/genair/... 🧵 1/n

AtomWorks is out! Building upon @biotite_python, we built a toolkit for all things biomolecules and trained RF3 with it. All open-source, test it via `pip install atomworks`! AtomWorks: github.com/RosettaCommo... RF3: github.com/RosettaCommo... Paper: tinyurl.com/y2w4z65b 1/6

An incredible project to witness, led by the most incredible dreamteam @ncorley.bsky.social , @simonmathis.bsky.social and Rohith Krishna with an amazing team inside and outside the Baker lab. Check it out and let us know what you think/contribute to the codebase! 6/6

AtomWorks is out! Building upon @biotite_python, we built a toolkit for all things biomolecules and trained RF3 with it. All open-source, test it via `pip install atomworks`! AtomWorks: github.com/RosettaCommo... RF3: github.com/RosettaCommo... Paper: tinyurl.com/y2w4z65b 1/6

`atomworks.ml` on the other hand offers advanced dataset featurization and sampling for deep learning workflows, all operating on the canonical AtomArray object from @biotite_python so that all transforms are traceable and generalizable between models. 4/6

AtomWorks is out! Building upon @biotite_python, we built a toolkit for all things biomolecules and trained RF3 with it. All open-source, test it via `pip install atomworks`! AtomWorks: github.com/RosettaCommo... RF3: github.com/RosettaCommo... Paper: tinyurl.com/y2w4z65b 1/6

In the past, every BioML model had its own data pipeline, creating loads of overhead. With AtomWorks, >80% of code is shared between models like ProteinMPNN, RF3 or design models. 2/6