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Lausanne-based Adaptyv raises €34.35 million Series A to build the automated lab for agentic biology. August 27, 2026 Adaptyv Bio, the Lausanne-based BioTech startup building an automated wet lab for AI-driven protein design, has raised a €34.35 million ($40 million) Series A round. The round was led by Highland Europe, with existing investors Ace Ventures, ByFounders, and Y Combinator doubling down. Ace Ventures led the company's €6.8 million ($8 million) Seed round in late 2024. "AI has made it easy to design proteins. Many teams can now create thousands of candidate molecules for a target in an afternoon. Finding out whether those molecules actually work is still the hard part. That requires a wet lab. But most wet labs were built for a world where scientists tested twenty designs per target, not two thousand. Adaptyv closes this gap," ACE Ventures said. Founded by Julian Englert and Daniel Nakhaee-Zadeh Gutierrez, Adaptyv has built "an automated, AI-native lab" where the process starts with a digital protein sequence and ends with real experimental data. DNA is synthesised in-house, proteins are expressed cell-free, and binding is measured using SPR with built-in quality control. Customers can submit their designs through a web platform, or through an API, allowing AI agents to order their own experiments directly. When ACE led Adaptyv's Seed round in November 2024, the startup had only nine people and a lab that was beginning to see commercial demand. Fast forward to 2026: the team has tripled, revenue has grown roughly 10x, the company has brought DNA synthesis in-house, scaled lab automation, and even launched its Proteinbase competition platform, attracting "680 participants and 10,000 protein designs to one competition." Adaptyv has also shipped a public API now integrated with Boltz, Chai, Cradle, Tamarind, Latent Labs and Benchling. It now counts over 100 customers, including frontier AI labs, top 5 pharma companies and AI-native drug discovery startups. The company recently ran a benchmark with Anthropic, sending 16 targets from its public design competitions to Claude Science and testing the resulting sequences, anonymised. According to the company, 95% of the 1,320 designs were expressed. "Out of 1,320 designs, 354 bound their target, giving Claude an average hit rate of 26.8%. Only a single target yielded 0 binders. Compared to the results of our Protein Design Competitions, Claude's designs had noticeably higher success rates and would have won 5 out 6, yielding tighter binders," reported the company The test showcased how Adaptyv is "becoming the place frontier labs go when they need real-world proof". The company noted that Google DeepMind runs a similar validation loop, Chai Discovery used Adaptyv to validate its Chai-2 zero-shot antibody release, and Roche and Novo Nordisk run therapeutic design cycles roughly 4x faster than their internal pipelines. The new capital will fund two types of growth: horizontal expansion of lab capacity and vertical expansion into harder biology problems. On the horizontal side, the startup plans to roughly triple its lab capacity by the end of 2026 and keep growing through 2027. On the vertical side, it intends to move beyond binding measurements, which is a market estimated to be worth "a few hundred million dollars a year", into developability and full biophysical characterisation, next-generation modalities such as peptides, ADCs and degraders, and eventually cell-based function. For the horizontal expansion, Adaptyv Bio has announced a new lab and office will open in London in the fourth quarter of 2026, alongside an expansion in Lausanne. The company plans to grow its team from 25 to around 60, with a focus on production, automation and software. For its founders, the long-term goal is to build a biological gigafactory with enough automated experimental capacity for AI to learn from physical reality as quickly as it learns from text. Adaptyv's goal is for lab throughput to no longer be the constraint on how fast biology can progress.

Bristol Myers Squibb, Chai partner on AI antibody discovery. Collaboration will use Chai Discovery's AI models to accelerate therapeutic antibody candidate identification. August 20, 2026 Associate Editor, Contract Pharma Editor's Take: Chai's agreement with Bristol Myers Squibb demonstrates the increasing speed of adoption of AI models by major pharmaceutical companies. Learn how AI is reshaping early drug development. Bristol Myers Squibb (BMS) has entered a collaboration with Chai Discovery, an AI company, to advance the discovery of therapeutic antibodies using artificial intelligence. Under the collaboration, Bristol Myers Squibb will leverage Chai's AI models and platform capabilities, including its molecular folding and design models, to support the discovery of antibody candidates across its portfolio and further its efforts to build an AI-powered, continuously learning discovery system. Chai Discovery uses AI to predict and reprogram molecular interactions, helping scientists design new biomolecules with specific properties. The company's models accelerate drug discovery by generating molecules based on defined criteria and compressing discovery cycles to pursue targets that traditional discovery methods have historically struggled to reach. "We're thrilled to partner with Bristol Myers Squibb to deploy our technology, including toward diverse drug targets," said Joshua Meier, co-founder and CEO of Chai Discovery. "By combining Chai's advanced AI models with Bristol Myers Squibb's deep therapeutic expertise, we hope to rapidly accelerate the timeline from concept to viable therapeutic candidates." Bristol Myers Squibb is the latest biopharmaceutical company to collaborate with Chai Discovery. In June, Pfizer entered a license agreement with Chai Discovery to accelerate drug discovery research.

Chai Discovery co-founders Josh Meier and Matt McPartlon are treating drug design as an engineering problem rather than traditional trial-and-error science. Their approach centres on scaling data, models, and compute to improve molecular design. The company has achieved notable results in antibody design, increasing hit rates from below 0.1% to 16%. Their goal is to reduce drug discovery timelines from nine months to nine days. The breakthrough builds on advances in protein folding and diffusion models. Previously, antibody design was considered too complex due to limited data. However, new architectures have made this possible. Meier, who worked at OpenAI on early GPT models, sees parallels between language learning and molecular design. Co-founder McPartlon, originally from theoretical computer science, emphasises simplification in model development. The team combines AI researchers with domain experts in chemistry and biology.