new blog post with Amy Lu ! we tell the story of how we figured out latent diffusion for all-atom protein co-generation, with our methods CHEAP and PLAID. link 👇

🚀 The @AAAI Symposium on Agentic AI for Science is coming up Mar 31–Apr 2 near SFO! Join us in person or online to hear from top voices in #agent, #LLM, and #science and present your work. 📢 We’re still accepting submissions! 🔗 aaaiagenticai.github.io Speakers include

Reliable algorithm selection for machine learning-guided design Prescient Design Genentech 1. This paper introduces a novel method for selecting machine learning-guided design algorithms that can reliably generate desired outputs meeting user-defined success criteria, such as

We have an opening in our group within Prescient Design, Genentech at the intersection of machine learning, molecular dynamics, and structure-based drug design. roche.wd3.myworkdayjobs.com/ROG-A2O-GENE/j…

Great work and collaboration led by Hanchen (Hanchen Wang) on agents for spatial genomics/biology! 👇

🚨New post on the Berkeley AI Research blog! Hopefully more accessible for a general ML audience: - Protein structure prediction vs. generation - All-atom protein generation as a multimodal generation problem - Our work on PLAID! With Nathan C. Frey Prescient Design ⬇

ascpt.onlinelibrary.wiley.com/doi/10.1111/ct… i co-authored a short tutorial-ish paper about LLMs on drug discovery. too many things have happened after we wrote it in the last 6 months though, lol

We are presenting 5 papers ICLR 2026 on AI4Bio / BioML: Interpretable LLMs, all-atom latent diffusion, Agents, & guided sampling from foundation models 🧵1/

I will be presenting this work tomorrow at #ICLR2025 at 10 am stop by to know how to build protein language models and use them to design proteins with new properties!

📢 Excited to present our poster at the #ICLR2025 GEMBio Workshop! I'll be introducing TherAbDesign - a novel, sequence-based framework that efficiently optimizes antibodies toward therapeutically relevant biophysical properties. [1/4]

Natasa Tagasovska talking about OOD for protein design!

Come say hi at my #AISTATS2025 poster tomorrow! 📍 Poster Session I #83 🕒 Sat, May 3 at 3pm Semiparametric Conformal Prediction uses copulas and the one-step estimator to build efficient conformal sets over multiple response variables. Joint work w/ Rob Tibshirani, Kyunghyun Cho

arxiv.org/abs/2411.02114

We're hiring! 🎉 Join our Prescient Design Genentech team in NYC! We're hiring a Principal Software Engineer to lead the development of our flagship AI/ML Lab-in-the-Loop platform for therapeutic molecular design 🧬 Drive impact w/ full-stack skills (esp. front-end) as part of

it's been more than a decade since KD was proposed, and i've been using it all along .. but why does it work? too many speculations but no simple explanation. Sungmin Cha and i decided to see if we can come up with the simplest working description of KD in this work. we ended

i was asked by a few (inc. Yuanqing Wang ) what i meant by this earlier tweet, and since i'm pretty busy, i decided to write a blog post to answer the question 🤦

✨ new work with Ji Won Park #icml2025! a zoo of algos exists for designing new proteins & molecules w/ AI🧬🧪how do you pick which one to use? our method selects design algos that will achieve user-specified, population-level success criteria w/ high-prob guarantees.👇

arxiv.org/abs/2503.20767 we want to select successful algos, formalized as giving a distribution of design labels that achieves a population-level criterion, e.g. >10% of designs beat wild type. AI can predict which algos succeed, but who knows when these predictions are correct.

Have you ever wondered how your specialized biomolecule engineering model compares with a general purpose LLM on bio-optimization tasks? Come check out our work at ICML, in the East poster session (#E-2804) happening right now!. #ICML2025

come by to chat about training LLMs to be black-box token sequence optimizers