Back in 2024, BindCraft1 emerged as one of the strongest methods in our protein design benchmarks, winning our first EGFR competition and becoming one of the most widely used approaches in the following round. We’re happy to see BindCraft2 released just in time for our new protein design competition with Anthropic, where more than 5,000 designs will be experimentally tested in our lab. Show us what you can do with BindCraft2!

We’re partnering with @Anthropic to launch the biggest Protein Design Competition in the world, challenging people around the world to use AI to design new potential drug candidates for diseases that affect millions of lives. The competition will feature five challenges, each focused on a specific disease or biological mechanism. Compared to previous competitions, it will be a big step-up in complexity and scale to push the boundaries of AI-driven protein design. Together with Anthropic, we’re sponsoring over $1 million in experimental validation, making it possible to test more than 5,000 protein designs in our automated lab at no cost to participants. Anthropic is providing an additional $1 million in Claude credits. All experimental results will be published openly on @Proteinbase, including designs that didn’t work, so anyone can access the data and build on what we learn. The competition is open to everyone and free to enter. It will feature 3 tracks: - Track 1 is aimed at expert protein designers, with up to 20 teams to be selected. - Track 2 is targeting life science academics and industry researchers. - Track 3 is open to everyone from tech enthusiasts to high-school students. By combining Anthropic’s models with access to our automated lab, we want to make it possible for anyone with a laptop and an internet connection to join the global effort to advance human health with AI. A big thanks to @Modal for contributing compute for protein design and to @TwistBioscience for contributing the DNA for the experimental validation! Sign up link below -

We're bringing experimental validation to @OpenAI's Rosalind Workbench through the Adaptyv API. Rosalind Workbench is a new way to orchestrate complex life science research workflows across models and specialized scientific tools. Through the Adaptyv API, those workflows can now run full design–build–test–learn cycles in our wet lab, with real experimental data feeding directly into the next round of designs.

We raised a $40M Series A to build the automated lab for agentic biology. Last week, we shared the work we did with @Anthropic: Claude designed proteins, sent them to our automated wet lab, and got real experimental data back. We believe this is where biology is going: AI agents designing experiments, running them in automated wet labs, learning from the results and iterating. But AI can only move as fast as the experiments behind it. To use the potential of AI to cure all diseases, we need to build high-throughput wet lab infrastructure: a “biological gigafactory”. That’s exactly what we’re doing at Adaptyv. Over the past year we’ve grown our lab throughput by over 5x and onboarded more than 100 customers, ranging from bio AI labs like @chaidiscovery and @boltz_bio to pharmas like @Roche and @novonordisk to dozens and dozens of new startups that use AI to radically speed up drug discovery.

The round was led by @HighlandEurope with existing investors ACE Ventures, @byFounders and @ycombinator doubling down.

AI has solved software. Biology is the next frontier. We're hiring across every team at Adaptyv. We’ve built the best automated lab for protein designers to experimentally test their AI-designed proteins. Today, the most advanced protein design companies run their wet-lab work on Adaptyv, from the biggest biopharmas to frontier AI labs to dozens of virtual biotech startups. Demand has grown faster than we have, so we’re hiring across the board: • Bio: Research associates, scientists and lab technicians to develop and run new assays at scale. • Lab automation: Engineers and interns to onboard new lab instruments and scale our automation infrastructure. • Software: Product and backend engineers to scale LabOS, our internal lab orchestration platform, our API for agents and the data pipelines that turn messy physical-world data into clean results. • Partnerships, customer success and operations: Building partnerships with AI & pharma labs, making sure customers understand their data and can run more campaigns, making sure the company operations run smoothly

What happens when you let frontier LLMs design proteins, and then synthesize and test them in a wet lab? We ran a protein design competition with @muni_bio where AI agents competed against humans to design molecules that bind TREM2, a key receptor linked to Alzheimer’s. Results: GPT 5.2 and Grok 4.1 both placed in the top 5, with molecules showing strong binding to TREM2 when tested in our lab.

Honest caveat: we measured binding, on one target, in one day, which is the easy half of a therapeutic. We'd go as far as to claim binding is roughly solved. Whether agent-grade design holds for developability, immunogenicity, PK/PD or in vivo potency, we don't know yet.