I'm a big admirer of what Eli Mann and Corin Wagen have built at rowansci.com in such a short time. Now they've released their first FMs as NNPs seem like they're at an inflection point. Really excited to dig in on Egret-1 and the future of NNPs. Come learn with us!

Our lab at the Weizmann Institute was hit tonight by Iran We will rebuild and return 💪

1/ 🧬 GenBio AI's AIDO.Protein-RAG (16B) now ranks #1 on ProteinGym, the most widely used benchmark for protein prediction.

Cells are dynamic, messy and context dependent. Scaling models across diverse states needs flexibility to capture heterogeneity Introducing State, a transformer that predicts perturbation effects by training over sets of cells Team effort led by the unstoppable Abhinav Adduri

Note that a lot of genome scale perturb seq datasets are also in this regime

I have been long arguing that a world model is NOT about generating videos, but IS about simulating all possibilities of the world to serve as a sandbox for general-purpose reasoning via thought-experiments. This paper proposes an architecture toward that arxiv.org/abs/2507.05169

x.com/i/article/1941…

We will be presenting our paper on Multimodal Benchmarking of Foundation Model Representations for Cellular Perturbation Response Prediction at two #ICML2025 Workshops this week: FM4LS (fm4ls.github.io) and Generative AI and Biology (genbio-workshop.github.io/2025/). We

🤔 What if protein design models knew where they’re uncertain and adapted their predictions accordingly? 🚀 We introduce uncertainty-aware discrete diffusion for structure-conditioned protein design: doi.org/10.1101/2025.0…. 📍Catch us at #ICML2025 FM4LS workshop this week! 1/4

Excited to share a milestone published in Nature Medicine from our decade-long effort to build The Human Phenotype Project, a unique longitudinal cohort with unmatched depth of clinical and multi-omic profiling, enabling truly predictive, personalized medicine. Led together with

1/ 🧬 We’re at #ICML2025! GenBio AI is presenting five papers on generative AI for biology across two major workshops. Research areas: protein design, tissue modeling, perturbation prediction, and multimodal FMs 🧵↓

1/ 🚀 Introducing AIDO.StructureDiffusion: A generative model for structural protein design—enabling high-quality, controllable generation of monomers, complexes, and antibodies. 🧵

Excited to talk about our work on evaluating *medically adapted* LLMs and VLMs, covering additional results since our oral presentation at EMNLP 2024. If you ever wondered whether Med-* LLMs/VLMs are really better than their general-domain counterparts, come check it out on 8/4!

Just released gpt-oss: state-of-the-art open-weight language models that deliver strong real-world performance. Runs locally on a laptop!

Recently, we used contextualized.ml to go beyond physical limits in biology and medicine, inferring n-of-1 models for 7997 of patients and generating models of unseen diseases on-demand. This hints at how to develop accurate and personalized biological simulators like AIDO.