Converge Bio raises $25M to scale AI-driven drug discovery

Converge Bio, a startup that helps pharmaceutical and biotechnology companies develop drugs faster using generative artificial intelligence trained on molecular data, has raised an oversubscribed $25 million Series A funding round. The financing was led by Bessemer Venture Partners.

Other investors in the round included:

• TLV Partners
• Saras Capital
• Vintage Investment Partners
• Executives from Meta, OpenAI, and Wiz

The new capital follows a $5.5 million seed round raised in 2024.

The company currently operates across the U.S., Canada, Europe, and Israel, and is expanding into Asia. According to co-founder and CEO Dov Gertz, Converge Bio has completed over 40 programs for more than a dozen pharmaceutical and biotechnology customers. The startup has introduced systems focused on antibody design, protein yield optimization, and biomarker and target discovery. To support this growth, the startup has scaled its team to 34 employees, up from nine employees in November 2024. The platform has already helped a partner boost protein yield by 4 to 4.5X in a single computational iteration.

The funding arrives amid a broader industry transition from traditional “trial-and-error” methods to data-driven molecular design. Gertz noted that while there was significant “skepticism” regarding AI in drug discovery when the company was founded a year and a half ago, that sentiment has faded. However, validating novel molecules computationally presents unique challenges compared to text-based AI. Gertz explained that while text-based “hallucinations” are typically easy to identify, validating a novel molecular compound can take weeks, making the cost of such errors much higher. To mitigate this, the company filters new molecules by pairing generative models with predictive ones.

Converge Bio does not rely on text-based models for core scientific understanding, using them only as support tools for literature navigation. Instead, the company trains its models on DNA, RNA, proteins, and small molecules, utilizing a mix of large language models, diffusion models, traditional machine learning, and statistical methods. Gertz envisions a future where every life-science organization will pair traditional wet labs—physical laboratory environments where chemicals and biological matter are tested—with computational generative labs to design molecules. “Our customers don’t have to piece models together themselves. They get ready-to-use systems that plug directly into their workflows,” said Dov Gertz, Converge Bio CEO and co-founder.

Why it matters

Converge Bio is securing new capital as competition in the AI-driven drug discovery space heats up, with the industry shifting from “trial-and-error” approaches to data-driven molecular design.