Profluent @ProfluentBio

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

2 weeks ago

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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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.”

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

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.

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…

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.

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/2026…

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-ai-benc…

📄 Read the full paper here: nature.com/articles/s4158… 🧑‍💻 And explore the tool: protein2pam.profluent.bio

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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Great event at @benchling! We're excited to be among so many great teams creating more and better medicines with AI. Thanks for having us, @sajithw

Benchling @benchling

5 months ago

🤩 It was a full house at Benchling last night! Over 200 biotech and biopharma leaders joined to hear how scientific models are driving real impact today — and what it means for the future of R&D. What stood out? - Scientific models are here, but the real challenge is adoption

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Our BD team just wrapped a fun year-end off-site (spoiler: we did an escape room!) Now we’re gearing up for the annual trek to JPM next month. If you’d like to connect and learn more about the intersection of AI and protein design—gene editing, antibodies, and beyond—reach out, our DMs are open.

The work will combine Ensoma’s powerful in vivo HSC engineering platform with Profluent’s frontier AI models for protein design to create the next generation of custom-designed editors and intelligent delivery platforms to unlock treatments previously out of reach.

Today we’re excited to announce a new strategic collaboration with @EnsomaBio to advance AI-designed base editors for hematopoietic stem cell (HSC) therapies. The goal: durable, one-time treatments that target the root causes of hematologic and immune diseases. businesswire.com/news/home/2025…

📅  Thu 4 Dec 4:30 - 7:30 PM PST 📍Exhibit Hall C,D,E #1702 🔗 neurips.cc/virtual/2025/l… 👀 Look for Aadyot, lead ML scientist at Profluent!

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The upshot is that larger models generate more viable proteins across more protein families, especially those under-represented in the training data. This unlocks design for therapeutic proteins, like ultra-compact gene editors, that were previously out of reach.

#NeurIPS2025 friends! Meet the Profluent team *today* to talk about ProGen3, our generative protein language model that’s solving problems in medicine and agriculture. We're sharing how we built ProGen3 and how we validated it with real wet-lab data.

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