🧬 😴 TIRED: Scaling protein models to billions of parameters hoping they'll memorize all of evolution and generalize beyond 🔥 WIRED: Smart retrieval-augmented models that dynamically access what they need from sequence databases

Now available as its peer-reviewed version in Nature: nature.com/articles/s41586-0…

Quite pleased to hear that one of my submitted proteins placed #5 out of 1200 that were tested in this lovely competition. The approach: pure rational design. Nice to see that human Microsoft Word is still competitive with state-of-the-art AI methods 🙂

Super excited to share that I will join @Princeton @omenndarlingbio this Fall as an assistant professor. We combine syn bio and AI to program macromolecules and cells for therapeutics engineering and drug target discovery. jianglab.bioengineering.prin…

New paper “Proteome-wide model for human disease genetics” is now live at Nature Genetics: rdcu.be/eRu7K popEVE (pop.evemodel.org) finds the needles in the haystacks of human genetic variation:

Reminder - PhD applications for OATML are now open The first funding deadline is December 2 - candidates interested in developing Bayesian deep learning methodology, applications of ML, AI security, and understanding ML methodology are encouraged to apply More info: oatml.cs.ox.ac.uk/apply.html

Thrilled to announce I'm starting as a Principal Investigator at #Aithyra in Vienna! We'll be developing generative models to understand cell biology and design proteins. I'm hiring PhDs, Postdocs, & Visiting Researchers! PhD applications by Sept 10: apply.cemm.at

Our paper on generalizable antibody-antigen binding affinity prediction has been featured on the cover of the August Issue of @NatComputSci! 📔🎉

I’ve thoroughly enjoyed reading two (VERY!) recent papers that model protein sequences by retrieving evolutionary information (dynamically) at inference time, and there's a lot to unpack! [1] arxiv.org/abs/2506.08954 [2] biorxiv.org/content/10.1101/… (1/n)

A generative artificial-intelligence tool has designed a synthetic CRISPR system that successfully edits human DNA go.nature.com/3JadkRy

1/5 Biological data is noisy, redundant, and ever-growing. 🗣️ In our new paper (first paper of my post doc!! ⚡️), we track model performance across 14 years of UniRef100 snapshots to ask: how does pLM performance scale with training data?

AI expands the repertoire of CRISPR-associated proteins for genome editing @NatureNV preview by @NotinPascal nature.com/articles/d41586-0… @thisismadani profluent.bio/ @jeffruffolo @AadyotB et al nature.com/articles/s41586-0…

1/4 🚀 Announcing the 2025 Protein Engineering Tournament. This year’s challenge: design PETase enzymes, which degrade the type of plastic in bottles. Can AI-guided protein design help solve the climate crisis? Let’s find out! ⬇️ #AIforBiology #ClimateTech #ProteinEngineering #OpenScience

Save the date! Machine Learning for Drug Discovery (MLDD) is happening soon on Monday 30 June, 2025. MLDD aims to bring together ML for drug discovery experts, innovators, and enthusiasts from the machine learning, biotechnology and drug discovery domains in London, UK to converge, exchange ideas, and forge new paths in revolutionizing therapeutic development. This year, we are very fortunate to feature keynotes by: - Charlotte Brunne (EPFL) - John Chodera (Achira / MSKCC) - Kexin Huang (Stanford University) - Jacob Kimmel (NewLimit) - Marinka Zitnik (Harvard University) To accommodate the worldwide community, the event will be held fully remotely this year - a link to join and further information are provided at the MLDD website (link in thread below!) Looking forward to seeing you there!

I wonder if the gym membership for ProteinGym covers RNAGym as well?? oh wait, it's all free and 100% open source!