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Out today in nature, a great overview by Helen Pearson covering our work and that of others on automating scientific literature search. nature.com/articles/d4158…

Inside the brain of a protein language model 🔍 A thread on reverse engineering neural networks: Some methods, challenges, and rabbit holes on the 20 amino acids—the building blocks of life.

Why are PhD students so obsessed with starting companies based on their dissertation topics? Like, your PhD project isn’t the pinnacle of your potential—it’s literally just the first manageable project your PI tossed your way. “bUt iP I GeNerAtEd dUrInG mY pHd—” My brother in

The English PhD discourse is basically a Rorschach test. You see how few people understand what goes into a doctorate. You see how few people think about the humanities as a key to being human. It's really sad to see people reduce all education to its market value. I don't want

This is big: in collaboration with E11 Bio, we are announcing today a new way to map brain circuits at scale. With improvements in AI and microscopy I think whole brain mouse and maybe human brain mapping will be feasible in ~5-10 years. The key ingredients are simple (but

The next Alphafold2 will be a predictive model of protein expression. I think it's possible, and Align to Innovate is going for it! 🧬 Every single protein engineering project I have ever worked on at one point or another ran into expression issues. 😠 It can't come soon enough! 🙏

breakthroughs of 2024

Explaining molecular discovery with deep learning: A platform for structural insight Deep learning is increasingly applied in molecular discovery, offering accurate predictions of compound properties. However, traditional models often operate as "black boxes," limiting their

Brilliant paper from Meta having the potential to significantly boost LLM's reasoning power. Why force AI to explain in English when it can think directly in neural patterns? Imagine if your brain could skip words and share thoughts directly - that's what this paper achieves

Check out our last pub. from Michael Fischbach lab! We discovered that skin commensal microbes induce systemic and local B cell responses upon colonization and took advantage of this knowledge to create topical vaccines using engineered skin microbes. Please read the thread for details.

Traditionally, antibiotic discovery has centered on bacteria and fungi—until now. For the first time, we present a systematic exploration of Archaea—a major yet underexplored branch of the tree of life—as a source of novel antimicrobial compounds. Using AI, we analyzed

Super thought-provoking piece by Andrew Dunn on Isomorphic. The whole company is a big middle finger to the almost universally held dogma in TechBio that data is the key bottleneck.

Now out in Cell! Stay tuned for exciting work coming out of my lab soon! Aaand we are #hiring #PhD students EPFL. Deadline is December 15. Check out our lab webpage for more information. cell.com/cell/fulltext/…

This piece by Arielle Samuelson explores how AI can accelerate #alternativeprotein innovation to offer consumers more choices while addressing the rising global demand for meat in a more sustainable and efficient way. Dig in 👉 bit.ly/4g6xGXG

[1/n] Does AlphaFold3 "know" biophysics and the physics of protein folding? Are protein language models (pLMs) learning coevolutionary patterns? You can try to guess the answer to these questions using mechanistic interpretability. But the thing is, more often than not, we know

looks like a hydrophobic peptide to me

imo this is one of the most exciting lines of immunology research in recent years

🆕 DisProt Release: Dec 2024 🆕 🧬 Age-related Disorders Proteins Dataset (45 proteins, 306 pieces of evidence). 🎯 Focus: IDPs in Neurodegenerative Diseases (e.g., Alpha-synuclein for Lewy body dementia). 📊 41 new proteins, 111 publications! Explore disprot.org now!

I'm grateful to ARCHER2 HPC Service for 2 years of HPC access during my PhD! Check out my blog on the guidance I received from their support service and a non-technical case study on my work that was recently published in the Biophysical Journal D. Flemming Hansen Gabi Heller Chris Lorenz

This could be an incredible revolution in Cosmology. The Dark Energy model of the universe, which won a Nobel Prize in 2011, may be completely wrong. The accelerating expansion instead is simply because time runs faster in the voids between galaxies. Let me explain: