Converge Bio secures $25M in funding from Bessemer and tech leaders

Artificial intelligence is rapidly advancing in drug discovery as pharmaceutical and biotech firms seek to shorten R&D timelines by years and improve success rates amid rising costs. Over 200 startups are now competing to integrate AI directly into research workflows, drawing increased investor attention. Converge Bio is the latest company capitalizing on this trend, securing fresh funding as competition intensifies in the AI-driven drug discovery sector.

The Boston- and Tel Aviv–based startup, which helps pharmaceutical and biotech companies accelerate drug development using generative AI trained on molecular data, has raised a $25 million oversubscribed Series A round led by Bessemer Venture Partners. TLV Partners and Vintage Investment Partners also participated, along with additional support from unnamed executives at Meta, OpenAI, and Wiz.

In practice, Converge trains generative models on DNA, RNA, and protein sequences, then integrates them into pharmaceutical and biotech workflows to expedite drug development.

“The drug-development lifecycle has distinct stages—from target identification and discovery to manufacturing, clinical trials, and beyond—and within each, there are experiments we can support,” Converge Bio CEO and co-founder Dov Gertz shared in an exclusive interview with TechCrunch. “Our platform continues to expand across these stages, helping bring new drugs to market more quickly.”

So far, Converge has launched customer-facing systems. The startup has already introduced three distinct AI systems: one for antibody design, one for protein yield optimization, and one for biomarker and target discovery.

“Consider our antibody design system as an example. It’s not just a single model. It consists of three integrated components. First, a generative model creates novel antibodies. Next, predictive models filter these antibodies based on their molecular properties. Finally, a docking system, which uses a physics-based model, simulates the three-dimensional interactions between the antibody and its target,” Gertz continued. The value lies in the system as a whole, not in any single model, according to the CEO. “Our customers don’t need to assemble models themselves. They receive ready-to-use systems that integrate directly into their workflows.”

The new funding arrives about a year and a half after the company raised a $5.5 million seed round in 2024.  

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Since then, the two-year-old startup has scaled rapidly. Converge has formed 40 partnerships with pharmaceutical and biotech companies and is currently managing about 40 programs on its platform, Gertz said. It collaborates with customers across the U.S., Canada, Europe, and Israel and is now expanding into Asia.

The team has also grown quickly, increasing from just nine employees in November 2024 to 34. Along the way, Converge has started publishing public case studies. In one, the startup helped a partner increase protein yield by 4 to 4.5 times in a single computational iteration. In another, the platform generated antibodies with exceptionally high binding affinity, reaching the single-nanomolar range, Gertz noted.

AI-driven drug discovery is gaining significant momentum. Last year, Eli Lilly partnered with Nvidia to build what the companies described as the pharmaceutical industry’s most powerful supercomputer for drug discovery. And in October 2024, the developers behind Google DeepMind’s AlphaFold project won a Nobel Prize in Chemistry for creating AlphaFold, the AI system capable of predicting protein structures.

When asked about this momentum and how it is influencing Converge Bio’s growth, Gertz said the company is witnessing the largest financial opportunity in the history of life sciences, with the industry shifting from “trial-and-error” methods to data-driven molecular design.

“We feel the momentum strongly, especially in our inboxes. A year and a half ago, when we founded the company, there was considerable skepticism,” Gertz told TechCrunch. That skepticism has disappeared remarkably quickly, thanks to successful case studies from companies like Converge and from academia, he added.

Large language models are attracting attention in drug discovery for their ability to analyze biological sequences and propose new molecules, but challenges like hallucinations and accuracy persist. “In text, hallucinations are usually easy to spot,” the CEO said. “In molecules, validating a novel compound can take weeks, so the cost is much higher.” To address this, Converge combines generative models with predictive ones, filtering new molecules to reduce risk and enhance outcomes for its partners. “This filtration isn’t perfect, but it significantly lowers risk and delivers better results for our customers,” Gertz added.

TechCrunch also inquired about experts like Yann LeCun, who remain skeptical about using LLMs. “I’m a huge fan of Yann LeCun, and I completely agree with him. We don’t rely on text-based models for core scientific understanding. To truly grasp biology, models must be trained on DNA, RNA, proteins, and small molecules,” Gertz explained.

Text-based LLMs are used only as auxiliary tools, for instance, to help customers navigate literature on generated molecules. “They’re not our core technology,” Gertz said. “We’re not tied to a single architecture. We use LLMs, diffusion models, traditional machine learning, and statistical methods when appropriate.”

“Our vision is for every life-science organization to use Converge Bio as its generative AI lab. Wet labs will always exist, but they’ll be complemented by generative labs that create hypotheses and molecules computationally. We aim to be that generative lab for the entire industry,” Gertz said.