iiinova — where your digital self keeps evolving

Christian Dallago @machine.learning.bio · 24/09/2026

Today I’m joining collaborators at a WEF/CEPI roundtable in New York to share our latest @nvidiabot.bsky.social AI for Life Sciences work. Featured in Nature: nature.com/articles/d41...

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 17/03/2026

AlphaFold database has entered the era of complexes. Together with NVIDIA, DeepMind and EBI, we use ColabFold, OpenFold and MMseqs2-GPU to predict ~31 million complexes (homo & hetro-dimers) resulting in 1.8 million high-quality predictions 📄 research.nvidia.com/labs/dbr/ass... 🌐 alphafold.ebi.ac.uk

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EMBL-EBI @ebi.embl.org · 16/03/2026

You asked, we listened. Millions of AI-predicted protein complex structures are now available in the #AlphaFold Database. This spans homodimers from 20 of the most studied species, including humans, as well as the World Health Organization’s priority pathogens list. www.ebi.ac.uk/about/news/t...

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Christian Dallago @machine.learning.bio · 26/02/2026

Five years ago, we released FLIP. The core question was: can ML models for protein fitness prediction generalize in the ways that actually matter for protein engineering, i.e. low data, extrapolation to more mutations, out-of-distribution sequences?

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Kevin K. Yang 楊凱筌 @kevinkaichuang.bsky.social · 25/02/2026

We made FLIP2, a protein fitness benchmark spanning seven new datasets, including enzymes, protein-protein interactions, and light-sensitive proteins, as well as splits that measure generalization relevant to real-world protein engineering campaigns.

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Christian Dallago @machine.learning.bio · 22/12/2025

Our latest protein family-based GenAI collection of tools and datasets, ProFam, is out now. Everything -- from data, training and inference code, to a 215M llama-based ProFam-1 are fully open sourced. 🧵

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Christian Dallago @machine.learning.bio · 27/10/2025

Another exciting opportunity, this time as a colleague at Duke! Join as tenure track assistant prof. in Cell Bio & let’s work on closing the gap between in-silico and in-vivo: www.nature.com/naturecareer... Important: application closes Nov 1st!!!

nature.com

Tenure-Track Assistant Professor Position –AI/ML for Cell Biology - Durham, North Carolina (US) job with Duke University School of Medicine | 12844591

Tenure-Track Assistant Professor Position –AI/ML for Cell Biology

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Christian Dallago @machine.learning.bio · 17/10/2025

Another opening: Senior Multiscale Biology Applied Research Scientist! nvidia.eightfold.ai/careers/job/... Are fascinated by fundamental data modalities across biology like RNA-seq, mass spec & want to build computational tools that harnessing data to build intelligence? Come: join the team!

nvidia.eightfold.ai

Senior Applied Research Scientist, Multiscale Biology | NVIDIA Corporation

Apply your expertise in engineering biology through algorithms and tools for genes, tissues, organisms, and populations. Conduct collaborative applied research in multiscale biology using deep learnin...

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Stephan Hacker @stephanhacker2.bsky.social · 16/07/2025

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

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César de la Fuente @delafuentelab.bsky.social · 11/07/2025

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/

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Martin Steinegger 🇺🇦 @martinsteinegger.bsky.social · 07/07/2025

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

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Christian Dallago @machine.learning.bio · 15/06/2025

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

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Stephan Hacker @stephanhacker2.bsky.social · 10/06/2025

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.

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Christian Dallago @machine.learning.bio · 03/06/2025

@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

machine.learning.bio

Marie Skłodowska-Curie Fellowship

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

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Ewa Szczurek @ewaszczurek.bsky.social · 07/04/2025

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...

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Karsten Kreis @karstenkreis.bsky.social · 04/03/2025

📢📢 "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... (1/n)

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Karsten Kreis @karstenkreis.bsky.social · 04/03/2025

🔸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* 🔥 (10/n)

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Christian Dallago @machine.learning.bio · 23/12/2024

Two major life updates: - I'm moving to Senior Applied Research Scientist in Digital Biology at NVIDIA (Jan '25) - I'm starting a new lab at Duke as Visiting Assistant Prof (early '25) Both roles focus on tackling hard problems in biological machine learning through collaborative research. Long 🧵

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CAPRI @capridock.bsky.social · 19/12/2024

After a busy CASP/CAPRI year we resume our rolling CAPRI rounds, announcing the 1st target of 2025. It consists of an antibody-glycan complex. The glycan is heptyl α-D-mannopyranoside. Registration for this target is now open. pdbe.org/capri

pdbe.org

The European Bioinformatics Institute < EMBL-EBI

EMBL-EBI

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Anne Carpenter @drannecarpenter.bsky.social · 18/12/2024

We've a postdoc opening for our lab at the Broad: Cambridge MA! Must have experience in toxicology + data science Work on the wonderful OASIS dataset we are producing... Cell Painting, transcriptomics, proteomics in various liver cell and tissue models! broad.io/mlcbpostdoc

Image of the job posting (see URL for full text instead)

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Kai Kupferschmidt @kakape.bsky.social · 18/12/2024

CDC just confirmed the first severe case of #H5N1 in the US in a patient in Louisiana. This virus seems to be the same genotype D1.1 that is spreading in birds at the moment (so not the cattle genotype B3.13) that severely sickened the teenager in Canada.

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Noelia Ferruz @noeliaferruz.bsky.social · 18/12/2024

Protein language models excel at generating functional yet remarkably diverse artificial sequences. They however fail to naturally sample rare datapoints, like very high activities. In our new preprint, we show that RL can solve this without the need for additional data: arxiv.org/abs/2412.12979

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Alfonso Valencia @alfonsovalencia.bsky.social · 10/12/2024

Starting the new BSC IA factory. Financed by Europe, Spanish & Catalan governments + contributions from Portugal, Turkey, Romania New HPC and AI resources at the service of companies (hospitals included) With Juan Cruz Sec Estado Núria Montserrat Consellera Mateo Valero Dir @bsc-cns.bsky.social

Starting the NEW BSC AI factory - cofounded by Europe and the Spanish, Catalan governments plus contributions from other countries.

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Kevin K. Yang 楊凱筌 @kevinkaichuang.bsky.social · 09/12/2024

We trained a model to co-generate protein sequence and structure by working in the ESMFold latent space, which encodes both. PLAID only requires sequences for training but generates all-atom structures! Really proud of @amyxlu.bsky.social 's effort leading this project end-to-end!

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Arne Elofsson @handle.invalid · 03/12/2024

AF3 BEST METHOD followed by cluspro but also some conversion errors

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Torsten Schwede @torstenschwede.bsky.social · 02/12/2024

Assessors’ conclusions of the 3D category (individual protein chains) of #CASP16 🧪

Conclusions of assessment of 3D category

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Peter Koo @pkoo562.bsky.social · 30/11/2024

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Roland Dunbrack 🏳️‍🌈 @rolanddunbrack.bsky.social · 05/01/2024

I've seen cases where the AF2 model is correct and the experimental structure has artifacts (e.g. AF2 model has disulfide bonds for surface receptor and PDB does not bc it was in reducing conditions). I've seen AF2 capture conformational states not in PDB but later validated by cryoEM.

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Diego del Alamo @delalamo.xyz · 03/01/2024

Major differences are also observed when using these models as training data. Hsu et al found that inverse folding methods trained on 10^4 to 10^5 experimental structures outperformed those trained on 10^7 alphafold models (table shows perplexities, lower is better). doi.org/10.1101/2022...

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Diego del Alamo @delalamo.xyz · 03/01/2024

By now it’s clear that alphafold models don’t always work for e.g., small molecule docking. In one study, no difference was observed between success w/ docking into pockets of DL-generated models and those of homology models, despite major improvements in RMSD of the former. doi.org/10.7554/elif...

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Martin Pacesa @martinpacesa.bsky.social · 30/11/2024

#CASP16 results are in! Template-based VFold seems to be lead method for nucleic acid structure prediction! AlphaFold2 and 3 still seem to be best methods for protein monomer and complex prediction.

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Arne Elofsson @handle.invalid · 28/11/2024

#CASP16 program is posted (so you can guess the "winners". Congratulations to Yang and Kihara who seems to have done well in RNA+Proteins. Also congratulations to AF3-server (i.e. me) who was selected to talk (i.e. most people did worse than the server). predictioncenter.org/casp16/doc/C... .

predictioncenter.org

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Diego del Alamo @delalamo.xyz · 27/11/2024

Chai-1, the open source AF3 clone, is now available with an Apache 2 license github.com/chaidiscover...

Chai-1 is released under an Apache 2.0 License, which means it can be used for both academic and commerical purposes, including for drug discovery.

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Cas @caswognum.nl · 27/11/2024

Wow! 🤩 This may be the most carefully documented dataset on @polarishq.bsky.social. Great work by @roman-bushuiev.bsky.social! Do I have any #MassSpec researchers in my 🦋 network yet? I would love to hear what you think! github.com/polaris-hub/... #mass #spectrometry #dataset #benchmark

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Milot Mirdita @milot.bsky.social · 27/11/2024

MMseqs2 Release 16 Highlights: GPU-accelerated search📄, ORF or new 6-frame translated search modes, contig taxonomy always keeps the longest ORF, bug fixes (reduced memory and higher sensitivity) and relicensed as MIT 📄 biorxiv.org/content/10.1... 💾 mmseqs.com and 🐍Bioconda 🖥️🧬🧶

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Reactome @reactome.org · 15/11/2024

Please consider spending a few moments of your day supporting our resource by providing your feedback to our team! The things we learn from the user survey is essential for our continued success! 🖥️🧬

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EMBL-EBI @ebi.embl.org · 25/11/2024

Our data resources underpin life science research and scientists worldwide use them on a regular basis: 🌍 101 million requests to our data resource websites on an average day 💻 36 million unique IP addresses annually Which of our resources have you used recently? 👀 www.ebi.ac.uk/about/our-im...

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Kresten Lindorff-Larsen @lindorfflarsen.bsky.social · 25/11/2024

If you are a PhD student and like protein disorder (or want to learn more), Birthe Kragelund @bbkrage.bsky.social and Kristian Strømgaard are organizing a PhD course on How IDPs work 🧶🧬🧪 Note the cost if you are not at a Danish university. Details: phdcourses.ku.dk/DetailKursus...

Flyer for the course Keeping up with the interactome - How IDPs work including a list of speakers:
Professor Tanja Mittag, St Jude Children’s Hospital, Memphis, US
Professor Kresten Lindorff-Larsen, BIO, SCIENCE, UCPH
Professor Ylva Ivarsson, Uppsala University, Sweden
Dr. Michael Wehr, Systasy Bioscience, Munich, Germany
Professor Zsuzsanna Dostztányi, Eötvös Loránd University, Hungary
Professor Kristian Strømgaard, HEALTH, UCPH
Professor Stefano Gianni, Sprienza University of Rome, Italy
Professor Per Jemth, Uppsala University, Sweden
Dr. Franziska Schöppe, Novo Nordisk A/S, Denmark
Professor Benjamin Schuler, University of Zurich, Switzerland
Professor Alfonse De Simone, University of Naples, Italy
Dr. Malene Ringkjøbing Jensen, CNRS, Grenoble, France
Alleyn Plowright, Chief Scientific Officer, Pangea Bio, London UK
Professor Birthe B. Kragelund, BIO, SCIENCE, UCPH

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Christian Dallago @machine.learning.bio · 19/11/2024

The best hope we at NVIDIA have to help dent progress in machine learning biology is to enable everyone to do it as efficiently as possible. We released BioNeMo Framework open-source to share our learnings with the community, and learn from everyone here about what we need to build for you to scale

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Christian Dallago @machine.learning.bio · 19/11/2024

Yeah, yeah, I know... but: gotta make that investment 4y ago pay off at some point, thanks 🦋 for allowing custom domains 😇 Promise I'll only post and re-post (machine) learning bio posts! Except for this one. Starting with the next,.. Oh and an announcement in December. 🤫

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Christian Dallago @machine.learning.bio · 18/11/2024

GPUs are *fast* at aligning protein sequences (and profiles!). 178x faster than JackHMMER! In ColabFold, 23x faster end-to-end compared to AlphaFold2 reaching the same accuracy! You get immediate speedups for all methods leveraging MSAs, from DCA, to PoET, and even AlphaFold3!

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