Christian Dallago

Looking forward to hearing about the potential of machine learning for #Biology and #DrugDiscovery from an industry perspective. Register for the Virtual @chembiotalks.bsky.social to hear the perspective of Chris Dallago (@machine.learning.bio) from Nvidia.
#ChemBio #Chemsky #ML #MachineLearning

I still feel criminal for the handle but the manuscript embodies it well.

Moore’s law applied to speed not accuracy. I don’t think fundamentally the discoveries we are after are entirely dependent on speed.

I think the better law here is garbage in garbage out.

In that sense, you can wait for better data/curation, but it’s also fun to take destiny in your own hands :)

Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence - Nature Communications Researchers used artificial intelligence to mine global venom proteomes and discovered novel peptides with antimicrobial activity. Several candidates showed efficacy against drug-resistant bacteria in...

(1/5) Venoms are a vast, largely untapped library of bioactive molecules—and our new paper in @natcomms.nature.com ‬ @natprot.nature.com reveals just how powerful they can be. 🐍⚡️

Excited to have participated in the 2025 Symposium on Generative AI in Molecule Discovery in beautiful Munich, along with amazing scientists and colleagues @machine.learning.bio, Francesca Grisoni, ‪@ewaszczurek.bsky.social‬‬, @fabiantheis.bsky.social and more... 🔬🤖 events.hifis.net/event/2015/

Folddisco finds similar (dis)continuous 3D motifs in large protein structure databases. Its efficient index enables fast uncharacterized active site annotation, protein conformational state analysis and PPI interface comparison. 1/9🧶🧬
📄 www.biorxiv.org/content/10.1...
🌐 search.foldseek.com/folddisco

Thanks to @pkoo562.bsky.social , @kevinkaichuang.bsky.social and Ananthan, and all of CSHL' staff for the help bringing this volume out. Hopefully, we could inspire all those fantastic biologists to reach out to their computational friends to take the next leap in their scientific discoveries.

With contributions from fantastic colleagues @martinsteinegger.bsky.social , @mikeinouye.bsky.social, @jlistgarten.bsky.social , @ideasbyjin.bsky.social, @michael-heinzinger.bsky.social, and many more, the first CSHL volume on ML for Protein Science and Engineering is out: lnkd.in/dQdgGPpp

If only there was something like try/catch in every programming/scripting language ever :P

The maddening bit is UniProt offers a handy URL that resolves whatever query you pass to it to a list of hits www.uniprot.org/help/api_que...

Whenever I used to implement search bars I’d use my dbs as autocomplete but one hop to that API endpoint to catch whatever the user inputs 🤷‍♂️

The final program is now online for the 5th Virtual @chembiotalks.bsky.social: web.cvent.com/event/60e9f3...

Looking forward to talks by Sarah O'Connor, Chengqi Yi, @machine.learning.bio, @cathleenzeymer.bsky.social, @kellychibale.bsky.social, Jennifer Prescher and @craigmcrews.bsky.social.

Marie Skłodowska-Curie Fellowship PostDoc Opportunity in AI for Protein Science Marie Skłodowska-Curie Fellowship

@michael-heinzinger.bsky.social and I are seeking talented postdocs to support for the Marie Skłodowska-Curie Fellowship! Join our international AI+biology team, collaborate on protein design, and access top labs in the US & EU. Interested? Apply by July 15! Details: machine.learning.bio/news/msca

Save the date for the Helmholtz Munich AI for Health Symposium 2025, devoted to the topic of Generative AI in Molecule Discovery!

July 4, 2025
Helmholtz Munich Campus in Neuherberg, Germany

What now?
Register and submit abstracts (deadline May 2, 2025!)
events.hifis.net/event/2015/r...

📢📢 "Proteina: Scaling Flow-based Protein Structure Generative Models"

#ICLR2025 (Oral Presentation)

🔥 Project page: research.nvidia.com/labs/genair/...
📜 Paper: arxiv.org/abs/2503.00710
🛠️ Code and weights: github.com/NVIDIA-Digit...

🧵Details in thread...

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🔸Proteina is a fantastic collaboration with wonderful colleagues at NVIDIA:

🔥 Tomas Geffner*, @kdidi.bsky.social*, Zuobai Zhang*, Danny Reidenbach, Zhonglin Cao, @jyim.bsky.social , Mario Geiger, @machine.learning.bio, Emine Kucukbenli, @arashv.bsky.social, @karstenkreis.bsky.social* 🔥

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Big time

Whelp

A DNA language model based on multispecies alignment predicts the effects of genome-wide variants - Nature Biotechnology A language model predicts the effects of genetic variants in the human genome.

A DNA language model based on multispecies alignment predicts the effects of genome-wide variants | Nature Biotechnology
www.nature.com/articles/s41...

Not yet, waiting for them visas

Thanks. Once in full steam, there will be (way!) more GPU MSA-kinda things. Can't wait!

Vielen Dank :) I'll never turn down luck, always in high demand!

Thanks :) Indeed, new territory for everyone involved, but glad to be laying the path for something that'll hopefully be repeatable and the best of both worlds!

Home

It will take me some time to settle into the two roles (and move to the 🇺🇸), so bear with me over the coming weeks/months as I will update on bits and pieces.In the meantime, I have set up a very primitive website for the Duke lab at machine.learning.bio

... but the blend of all those ingredients, and the influence of great collaborators, both local and international, in academia and in industry.

The communality between the new NVIDIA role and the Duke lab will be to focus on research challenges that deserve — but are not currently under — the spotlight. Hard problems! Where the difference won’t be the machine learning approach, or the software approach, or the one dataset,..

In that spirit, my group at Duke will focus on advancing our understanding of biology through software and machine learning, and we’ll be working extensively with Parnam, Rohit and… As a fun added bonus, @alextong.bsky.social! Who is also joining as PI in 2025!

I'm thrilled to join @pranam.bsky.social and @rohitsingh8080.bsky.social. Duke has a unique blend of translational scientists that can bring ideas from the wetlab, to software and ML, back into the wetlab, all in service of characterising, engineering and programming biology!

Likewise, I’m extremely thankful to David Page and @scottsoderling.bsky.social at Duke, who have recognised that biology and information sciences are increasingly intertwined, with software programmable biology becoming a reality through ML informed by passionate and knowledgable (cell) biologists.

In particular, thank you to @anthonybcosta.bsky.social and Kimberly Powell, who at NVIDIA embraced the mission of Digital Biology Research to engage in highly collaborative research activities with the community to build the next generation of accelerated biology computation tools.

These opportunities didn’t just materialise out of thin air either, they took incredible courage and energy both by leadership at NVIDIA and Duke, which deserve my heartfelt appreciation for their patience and trust.