Artificial intelligence is moving quickly into drug discovery as pharmaceutical and biotech firms look for methods to chop years off R&D timelines and increase the possibilities of success amid rising cost. More than 200 startups at the moment are competing to weave AI directly into research workflows, attracting growing interest from investors. Converge Bio is the newest company to ride that shift, securing recent capital as competition within the AI-driven drug discovery space heats up.
The Boston- and Tel Aviv–based startup, which helps pharma and biotech firms develop drugs faster using generative AI trained on molecular data, has raised a $25 million oversubscribed Series A round, led by Bessemer Enterprise Partners. TLV Partners and Vintage Investment Partners also joined the round, together with additional backing from unidentified executives at Meta, OpenAI, and Wiz.
In practice, Converge trains generative models on DNA, RNA, and protein sequences then plugs them into pharma and biotech’s workflows to hurry up drug development.
“The drug-development lifecycle has defined stages — from goal identification and discovery to manufacturing, clinical trials, and beyond — and inside each, there are experiments we will support,” Converge Bio CEO and co-founder Dov Gertz said in an exclusive interview with TechCrunch. “Our platform continues to expand across these stages, helping bring recent drugs to market faster.”
Thus far, Converge has rolled out customer-facing systems. The startup has already introduced three discrete AI systems: one for antibody design, one for protein yield optimization, and one for biomarker and goal discovery.
“Take our antibody design system for instance. It’s not only a single model. It’s made up of three integrated components. First, a generative model creates novel antibodies. Next, predictive models filter those antibodies based on their molecular properties. Finally, a docking system, which uses physics-based model, simulates the three-dimensional interactions between the antibody and its goal,” Gertz continued. The worth lies within the system as an entire, not any single model, in line with the CEO. “Our customers don’t must piece models together themselves. They get ready-to-use systems that plug directly into their workflows.”
The brand new funding comes a few 12 months and a half after the corporate raised a $5.5 million seed round in 2024.
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Since then, the two-year-old startup has scaled quickly. Converge has signed 40 partnerships with pharmaceutical and biotech firms and is currently running about 40 programs on its platform, Gertz said. It really works with customers across the U.S., Canada, Europe and Israel and is now expanding into Asia.
The team has also grown rapidly, increasing to 34 employees from just nine in November 2024. Along the way in which, Converge has begun publishing public case studies. In a single, the startup helped a partner boost protein yield by 4 to 4.5X in a single computational iteration. In one other, the platform generated antibodies with extremely high binding affinity, reaching the single-nanomolar range, Gertz noted.
AI-driven drug discovery is experiencing a surge of interest. Last 12 months, Eli Lilly teamed up with Nvidia to construct what the businesses called the pharma industry’s strongest 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 that may predict protein structures.
When asked concerning the momentum and the way it’s shaping Converge Bio’s growth, Gertz said that the corporate is witnessing the biggest financial opportunity within the history of life sciences and the industry is shifting from “trial-and-error” approaches to data-driven molecular design.
“We feel the momentum deeply, especially in our inboxes. A 12 months and a half ago, once we founded the corporate, there was loads of skepticism,” Gertz told TechCrunch. That skepticism has vanished remarkably quickly, because of successful case studies from firms like Converge and from academia, he added.
Large language models are gaining attention in drug discovery for his or her ability to investigate biological sequences and suggest recent molecules, but challenges like hallucinations and accuracy remain. “In text, hallucinations are often easy to identify,” the CEO said. “In molecules, validating a novel compound can take weeks, so the associated fee is far higher.” To tackle this, Converge pairs generative models with predictive ones, filtering recent molecules to scale back risk and improve outcomes for its partners. “This filtration isn’t perfect, but it surely significantly reduces risk and delivers higher outcomes for our customers,” Gertz added.
TechCrunch also asked about experts like Yann LeCun, who remain skeptical about using LLMs. “I’m an enormous fan of Yann LeCun, and I completely agree with him. We don’t depend on text-based models for core scientific understanding. To really understand biology, models must be trained on DNA, RNA, proteins, and small molecules,” Gertz explained.
Text-based LLMs are used only as support tools, for instance, to assist 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 it is smart.”
“Our vision is that each life-science organization will use Converge Bio as its generative AI lab. Wet labs will at all times exist, but they’ll be paired with generative labs that create hypotheses and molecules computationally. We wish to be that generative lab for your entire industry,” Gertz said.
