💡IMO one of the best use of AI Scientist is to reanalyze data to find new insights. Introducing #CellVoyager: AI Compbio Agent that makes new discoveries by autonomously analyzing papers/data, which we then validate🚀 New findings on aging, Covid, scRNAseq etc. Open source!

Excited to announce that I’m starting a new position as a senior fellow FutureHouse. I’ll be working on applying AI agents to metagenomic workflows. Very grateful for the opportunity to learn and explore my scientific interests. Please reach out if you’d like to collaborate!

Evo 2 update: new dependency versions (torch, transformer engine, flash attn) and a docker option mean it should be easy to setup without needing to compile locally. Happy ATGC-ing! github.com/ArcInstitute/e…

In 1965, Margaret Dayhoff published the Atlas of Protein Sequence and Structure, which collated the 65 proteins whose amino acid sequences were then known. Inspired by that Atlas, today we are releasing the Dayhoff Atlas of protein sequence data and protein language models.

We are happy to announce that our latest manuscript has been published in Nature Communications! We identified the highly abundant and prevalent giant extrachromosomal element in the human mouth, named "Inocle". nature.com/articles/s4146…

Arc Institute trained their foundation model Evo 2 on DNA from all domains of life. What has it learned about the natural world? Our new research finds that it represents the tree of life, spanning thousands of species, as a curved manifold in its neuronal activations. (1/8)

Can protein LMs reveal scientific knowledge? We start by asking how pLMs turn sequences into structure signals. We map a contact prediction circuit: early motif features gate later domain features. Spurious or science? We can now test. 🧵(1 of N)

🌎👩‍🔬 For 15+ years biology has accumulated petabytes (million gigabytes) of🧬DNA sequencing data🧬 from the far reaches of our planet.🦠🍄🌵 Logan now democratizes efficient access to the world’s most comprehensive genetics dataset. Free and open. doi.org/10.1101/2024.0…

Many of the most complex and useful functions in biology emerge at the scale of whole genomes. Today, we share our preprint “Generative design of novel bacteriophages with genome language models”, where we validate the first, functional AI-generated genomes 🧵

Welcome to the age of generative genome design! In 1977, Sanger et al. sequenced the first genome—of phage ΦX174. Today, led by Samuel King, we report the first AI-generated genomes. Using ΦX174 as a template, we made novel, high-fitness phages with genome language models. 🧵

Had fun solving the cryo structure of Evo-Φ36 with the awesome team of Samuel King, Claudia Driscoll, and Max Wilkinson! Samuel is a beast and the reason there is a 'skevophage' react on Stanford slack.

The ability to design antibodies against any protein of interest has major implications for medicine, biotech, and basic science. Today, we introduce Germinal, a pipeline for epitope-targeted de novo antibody design achieving  4–22% success rates with efficient experimental

Today, we report Germinal, a method for efficient de novo antibody design, with Santiago Mille and Xiaojing Gao. Germinal achieves success rates of 4-22% across diverse epitopes. We make the work fully open, without doing lame things like posting a preprint without methods. 🧵

Super excited to share what we’ve been working on in collaboration with Xiaojing Gao over the past few months on de novo antibody design. Check out this great thread by our team lead Santiago Mille highlighting the technical aspects of the pipeline!

Entering my PhD, de novo antibody design was a grand challenge I thought would not be solved without huge increases in affinity data and Ab-Ag structure. Only 2 years later, we provide the first open-source recipe to get antibody binders, almost magically, out of a computer (1/3)

Published! 🎉 Paper now has more feature analysis and higher quality figures - thanks to great reviewer feedback! Code also got a major upgrade - v1.0.0 is way more modular so you can easily swap in different protein embeddings or SAE architectures: github.com/ElanaPearl/Int…

Excited to share Phalanx, our new layer for sequence modeling! Each block communicates with its neighbor, like the shield cover of a neighboring hoplite. Phalanx can replace sliding window attention and trains faster than optimized baselines while maintaining quality.

🚀 Excited to share our new paper: CGBench — Benchmarking Language Model Scientific Reasoning for Clinical Genetics Research Can AI truly understand scientific papers? We explore how LLMs interpret real biomedical literature — not just multiple-choice questions.🧵

Genome maintenance by telomerase is a fundamental process in nearly all eukaryotes. But where does it come from? Today, we report the discovery of telomerase homologs in a family of antiviral RTs, revealing an unexpected evolutionary origin in bacteria. doi.org/10.1101/2025.1…

We're thrilled to announce SeqHub, an AI-enabled platform for biological sequence analysis. SeqHub brings together sequence search, genome annotation, and data sharing in one place. I dreamed of a single place where I could learn everything about my sequences. Today, a much more