@ProfluentBio profile picture
Profluent @ProfluentBio

Grounded in nature, authored by AI

Joined September 2022
  • Posts 178
  • Following 0
  • Followers 4,133
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Profluent
 @ProfluentBio
19 Nov 2025
Today we’re announcing $106M in new funding led by Altimeter Capital and Bezos Expeditions. This brings our total to $150M to scale our frontier AI models which make biology programmable. Our frontier models have generated functional proteins (Nature Biotech, 2023), created the first CRISPR system designed from scratch (Nature, 2025), and showed clear scaling behavior (NeurIPS spotlight, 2025). The opportunities ahead are unimaginable. If you’re excited by shaping the future of biology – join us in pushing the science forward. Forbes: forbes.com/sites/amyfeldman/… Press Release: businesswire.com/news/home/2… -- Nature Biotech, 2023: nature.com/articles/s41587-0… NeurIPS spotlight, 2025: biorxiv.org/content/10.1101/… Nature, 2025: nature.com/articles/s41586-0…
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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/yWQpAvLiQ1KfM85u7 Or want to hang out and chat over dinner? Reserve a spot at our table: luma.com/qpc1mfw9

Meet Profluent at ICML 2026 in Seoul!

Fill this form out to meet with the Profluent team! We're generally interested in chatting about language models for protein design. At ICML, we'll be sharing more about the capabilities of our...

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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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P.S. Yes, we are hiring! job-boards.greenhouse.io/pro…

Profluent

Profluent is an AI-first protein design company. Founded in 2022, we develop deep generative models to design and validate novel, functional proteins to revolutionize biomedicine. Based in Emeryvil...

job-boards.greenhouse.io
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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
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Nathan Benaich
 @nathanbenaich
Jun 18
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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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.
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Nathan Benaich
 @nathanbenaich
Jun 18
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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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)
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Nathan Benaich
 @nathanbenaich
Jun 18
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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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.
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Nathan Benaich
 @nathanbenaich
Jun 18
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 reposted
Nathan Benaich
 @nathanbenaich
Jun 18
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 reposted
Ali Madani
 @thisismadani
May 26
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
May 25
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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Profluent reposted
Nathan Benaich
 @nathanbenaich
May 25
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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Profluent reposted
Ali Madani
 @thisismadani
May 20
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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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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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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Today we announced a landmark partnership with @EliLillyandCo to use our AI models to design recombinases for genetic medicine—a collaboration valued at up to $2.25 billion before royalties. The goal: use Profluent's AI models to design recombinase editors capable of inserting long stretches of DNA at precise locations in the genome. Read the press release for more: businesswire.com/news/home/2…
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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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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/2…

Profluent Announces Strategic Partnership with Lilly to Develop AI-Designed Recombinases for...

Profluent, the frontier AI company pioneering large-scale foundation models for protein design, today announced a multi-program strategic research collaborat...

businesswire.com
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What do AI-designed proteins look like in the field? 🌾🚜 On Monday, @thisismadani joins @Corteva at @WorldAgriTech to share how Profluent's AI models are engineering proteins for gene editing solutions that address real problems farmers face. March 16 · 12:30pm · San Francisco · World Agri-Tech worldagritechusa.com/agenda-…

Case Study // From AI‑Designed Molecules to Innovative Solutions for Farmers

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We’re excited to share our latest work published today in @NatureBiotech: Protein2PAM, an AI model that enables the rapid design of CRISPR editors with new PAM recognition And we’re making the model freely available for research and commercial use: protein2pam.profluent.bio
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📄 Read the full paper here: nature.com/articles/s41587-0… 🧑‍💻 And explore the tool: protein2pam.profluent.bio

Customizing CRISPR–Cas PAM specificity with protein language models

Nature Biotechnology - The protospacer-adjacent motif specificity of several Cas enzymes is customized using protein language models.

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