We’re partnering with @BioMarin to use our AI models to design novel medicines. Our foundation models unlock proteins with new and improved functions by drawing on the world's largest dataset of unique protein sequences (115B and counting): Profluent’s Protein Atlas BioMarin will screen designs we generate and advance the strongest into preclinical testing. Looking forward to what AI can unlock for patients!

Stephen Nayfach, our Head of Bioinformatics and Platform R&D, is at @CSHL this week for the Genome Engineering: CRISPR Frontiers event. Find him and say hi if you're there! 👋 He's giving a talk on using our AI models to design CRISPR-Cas9 variants for base editing ("Programming PAM Specificity for Mutation-Specific Base Editor Design") and also chairing the session "CRISPR Technologies I"

The Profluent team is heading to Summer RosettaCon next week! Find @jeffruffolo, @richardwshuai, @ShiakiMinami, and Alex Hoffnagle and say hi 👋 You can hear Richard talk about E1, our encoder model, on Thur at 1:30pm (“E1: Retrieval-Augmented Protein Encoders for Fitness and Structure Prediction”) and Alex share his work on our AI-designed base editors on Mon at 7:30pm (“Design of Programmable Base Editors with Protein Language Models”).

A suite of AI-designed base editors, all from one scaffold. The only piece unique to each patient is the guide RNA. That's the shift AI unlocks: rare disease moves from 'one drug at a time' to a platform, built to bring cost down and reach more patients. More on our work with GEMMABio, now backed by @ARPA_H's THRIVE program, below.

We're still in the GPT 1.5 era of AI × biology The early models already work (signal: our $2.25B Lilly partnership) but we're nowhere near the ceiling We're speedrunning to GPT 5 as fast as we can @thisismadani with @nathanbenaich @airstreet

Biology is no less complex than text and (in our biased opinion) way more impactful. Yet a fraction of the world's AI talent and compute is pointed at it. The field is wildly undersaturated. Watch @thisismadani chat with @nathanbenaich @airstreetcapital about the opportunity.

OpenCRISPR was the first demonstration that AI could design a genome editor from scratch. We used our AI model to build a protein that doesn't exist in nature for a specific function and it worked. Watch @thisismadani chat with @nathanbenaich @airstreetcapital about where we’ve gone from there (and where we’re going)

Our partnership with Eli Lilly carries up to $2.25B in milestones. The bigger story is the unlock behind it: large gene insertion, a problem AI makes solvable for the first time. Watch @thisismadani in conversation with @nathanbenaich @airstreet.

At PEGS Boston? Don't miss our Lead Protein Design Scientist Jeliazko Jeliazkov presenting "Designing Optimal Proteins at Scale" Generating proteins that are simultaneously optimal across many properties (affinity, stability, developability, and beyond) is a hard problem. Jeli's sharing our work on alignment of our foundational AI models as a path to multi-parameter protein optimization, with applications from gene editors to antibodies. Interested in learning more about our multi-parameter optimization work? Shoot us a DM.

We're at @ASGCTherapy today sharing something we've been heads down building: using our AI models to scale base editing for personalized medicine. The gap between what's theoretically correctable and what we can actually fix today is huge. We think AI can close that gap. Not there? Peter Cameron, our SVP of Gene Editing, breaks it down here. Interested in learning more? Shoot us a DM.