Boston- and Tel Aviv-based Converge Bio has raised $25 million in an oversubscribed Series A funding round to expand its generative AI platform for drug discovery.
The round was led by Bessemer Venture Partners, with participation from TLV Partners, Saras Capital, and Vintage Investment Partners. The financing also included strategic backing from senior executives affiliated with Meta, OpenAI, and Wiz.
Converge Bio develops AI systems designed to integrate directly into pharmaceutical and biotech research workflows, helping teams reduce development timelines and improve the likelihood of success in early-stage drug programs.
AI models trained directly on biological data
Rather than relying on general-purpose language models, Converge Bio trains its AI on DNA, RNA, and protein sequence data. These models are embedded into specific stages of the drug-development lifecycle, from target discovery to molecule optimization and manufacturing-related challenges.
According to the company, this biology-first approach allows its systems to deliver more reliable outputs and practical value in real-world research environments.
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System-level approach to drug development workflows
Converge Bio has launched multiple customer-facing AI systems, including platforms for antibody design, protein yield optimization, and biomarker and target discovery.
Each system combines generative models, predictive filtering, and physics-based simulations to support decision-making across molecular design and validation. The company positions these systems as fully integrated solutions that customers can deploy without assembling separate AI components themselves.
Commercial traction and rapid scaling
The Series A follows a $5.5 million Seed round raised in 2024. Since then, Converge Bio has completed more than 40 research programs with pharmaceutical and biotech partners across North America, Europe, and Israel, with expansion now underway in Asia.
The team has grown to more than 30 employees, and the company has begun publishing case studies highlighting measurable improvements in protein yield and binding affinity achieved through its platform.
Reducing risk in AI-driven molecular design
One of the key challenges in applying AI to drug discovery is validation cost. Unlike text outputs, molecular predictions can take weeks to confirm experimentally. Converge Bio addresses this by pairing generative models with multiple layers of predictive filtering to reduce the likelihood of low-quality candidates reaching the lab.
The company combines different model types, including diffusion models, classical machine learning, and statistical methods. Text-based language models are used only as supporting tools, not as the foundation of scientific reasoning.
Positioning generative AI as a core research layer
Converge Bio sees its platform as a future standard component of life science R&D. While physical laboratories remain essential, the company believes computational “generative labs” will increasingly define how hypotheses and molecules are created before experimental testing begins.
As pharmaceutical research shifts toward data-driven molecular design, Converge Bio aims to become the AI infrastructure layer supporting faster, more efficient drug discovery across the industry.
