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Profluent@ProfluentBio·9 Tem

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.

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95% of rare diseases have no approved treatment. When your child is getting sicker, waiting isn’t an option. ARPA-H is investing $160 million through THRIVE to accelerate personalized genetic cures for the kids who need them most. A rare diagnosis should never mean no hope for a cure. @broadinstitute @ChildrensPhila @igisci @StJude @Stanford #GEMMABio #MassachusettsGeneralHospital arpa-h.gov/news-and-event…

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Profluent@ProfluentBio·9 Tem

The bottleneck in rare disease is design: too many mutations, one editor at a time. AI unlocks that. Proud to be building GemmaBio's platform with them under @ARPA_H 's THRIVE program. We're bringing our frontier AI models and base editors to the work to design modular gene editors that scale across many mutations.

ARPA-H@ARPA_H

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Profluent@ProfluentBio·2 Tem

@thisismadani P.S. Yes, we are hiring! job-boards.greenhouse.io/profluent

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Profluent@ProfluentBio·2 Tem

And here's a few places you can find us during the conference: — Oral talk: @thisismadani with collaborators from NVIDIA and Microsoft Research: "FLIP2: Expanding Protein Fitness Landscape Benchmarks for Real-World Machine Learning Applications" (Tue, Jul 7 @ 10:15am, Hall D2) — Workshop: Join @jeffruffolo, @AadyotB, and the Profluent team: "E1: Retrieval-Augmented Protein Encoder Models" (Sat, Jul 11 @ 2:25pm, Room S317) — And our very own @park_jungy will be presenting work from his PhD: "Discovering Symmetry Groups with Flow Matching" (Wed, Jul 8 @ 5pm, Hall A #3010) and "Smoothness Errors in Dynamics Models and How to Avoid Them" (Thu, Jul 9 @ 2:30pm, Hall A #2200)

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Profluent@ProfluentBio·2 Tem

The Profluent team will be at ICML in Seoul next week. Find us to chat, grab a coffee, or join us for dinner! We’d love to talk shop (protein language models, protein optimization, sequence-first methods vs structure-first). Basically, anything at the intersection of AI and biology. Want to grab coffee? Let us know here: forms.gle/yWQpAvLiQ1KfM8… Or want to hang out and chat over dinner? Reserve a spot at our table: luma.com/qpc1mfw9

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Profluent@ProfluentBio·26 Haz

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

it's been a huge few weeks for ai in bio: a $2.25b @profluentbio x @elilillyandco deal on ai-designed gene editors, verve's base-editing data, new scaling results on protein models from @czbiohub, @isomorphiclabs' haul. @thisismadani and i recorded a pod diving into all of it we get into taking biology from discovery to design, sequence-first vs structure-first, and why he calls this the "gpt-1.5 era" of biology... enjoy!

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Profluent@ProfluentBio·25 Haz

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.

it's been a huge few weeks for ai in bio: a $2.25b @profluentbio x @elilillyandco deal on ai-designed gene editors, verve's base-editing data, new scaling results on protein models from @czbiohub, @isomorphiclabs' haul. @thisismadani and i recorded a pod diving into all of it we get into taking biology from discovery to design, sequence-first vs structure-first, and why he calls this the "gpt-1.5 era" of biology... enjoy!

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Profluent@ProfluentBio·24 Haz

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)

it's been a huge few weeks for ai in bio: a $2.25b @profluentbio x @elilillyandco deal on ai-designed gene editors, verve's base-editing data, new scaling results on protein models from @czbiohub, @isomorphiclabs' haul. @thisismadani and i recorded a pod diving into all of it we get into taking biology from discovery to design, sequence-first vs structure-first, and why he calls this the "gpt-1.5 era" of biology... enjoy!

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Profluent@ProfluentBio·23 Haz

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.

it's been a huge few weeks for ai in bio: a $2.25b @profluentbio x @elilillyandco deal on ai-designed gene editors, verve's base-editing data, new scaling results on protein models from @czbiohub, @isomorphiclabs' haul. @thisismadani and i recorded a pod diving into all of it we get into taking biology from discovery to design, sequence-first vs structure-first, and why he calls this the "gpt-1.5 era" of biology... enjoy!

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Nathan Benaich@nathanbenaich·18 Haz

it's been a huge few weeks for ai in bio: a $2.25b @profluentbio x @elilillyandco deal on ai-designed gene editors, verve's base-editing data, new scaling results on protein models from @czbiohub, @isomorphiclabs' haul. @thisismadani and i recorded a pod diving into all of it we get into taking biology from discovery to design, sequence-first vs structure-first, and why he calls this the "gpt-1.5 era" of biology... enjoy!

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Ali Madani@thisismadani·26 May

can AI scale it? at ASGCT this month, we showed AI can ~10x the addressable coverage when designing base editors (the molecules behind the Verve/Lilly announcement), among other benefits. that translates to an unlock for patients by dramatically expanding addressable targets for therapeutic gene editing we don't want a world where this is a one off breakthrough. we want a repeatable engine.

Crémieux@cremieuxrecueil

Eli Lilly has done it. They've gone and made what seems to be a powerful, permanent gene therapy for LDL cholesterol. That means they'll be able to effectively prevent most heart disease with a single infusion!

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Nathan Benaich@nathanbenaich·26 May

we live in the future - the next step is designing large gene insertions and fine scale editing with AI 👀 @ProfluentBio “One dose of VERVE-102 (in vivo base editor) led to dose-dependent, substantial, and sustained reductions in PCSK9 and LDL cholesterol levels.”

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Ali Madani@thisismadani·20 May

cool to see 3 nature papers published in one day on AI for science. contrary to AI replacement doomerism, i firmly see the future being defined by the scientist. incredible time to build previous decades focused builder energy on social media or enterprise SaaS. this is insanely more exciting and impactful. drug discovery clearly is one of the largest impact areas. i hope it will extend beyond that as well

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Profluent@ProfluentBio·14 May

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.

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Profluent@ProfluentBio·14 May

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.

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Profluent@ProfluentBio·28 Nis

Our frontier AI models design custom recombinases from scratch, programmable to target virtually any location in the genome. We're collaborating with @EliLillyandCo to turn that capability into medicines. Read the press release for more: businesswire.com/news/home/2026…

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Profluent@ProfluentBio·28 Nis

This has been a long sought goal in the gene editing field, but current tools can't reliably make insertions at that scale. Naturally occurring recombinases could but are limited in where they can act and traditional metagenomic discovery and protein engineering approaches can't precisely control their targeting.

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