Synthetic intelligence is transferring rapidly into drug discovery as pharmaceutical and biotech corporations search for methods to chop years off R&D timelines and enhance the possibilities of success amid rising value. Extra than 200 startups at the moment are competing to weave AI straight into analysis workflows, attracting rising curiosity from traders. Converge Bio is the newest firm to journey that shift, securing new capital as competitors within the AI-driven drug discovery house heats up.
The Boston- and Tel Aviv–based mostly startup, which helps pharma and biotech corporations develop medicine quicker utilizing generative AI skilled on molecular knowledge, has raised a $25 million oversubscribed Collection A spherical, led by Bessemer Enterprise Companions. TLV Companions and Classic Funding Companions additionally joined the spherical, together with extra backing from unidentified executives at Meta, OpenAI, and Wiz.
In apply, Converge trains generative fashions on DNA, RNA, and protein sequences then plugs them into pharma and biotech’s workflows to hurry up drug improvement.
“The drug-development lifecycle has outlined phases — from goal identification and discovery to manufacturing, scientific trials, and past — and inside every, there are experiments we are able to help,” Converge Bio CEO and co-founder Dov Gertz stated in an unique interview with TechCrunch. “Our platform continues to develop throughout these phases, serving to deliver new medicine to market quicker.”
To date, Converge has rolled out customer-facing methods. The startup has already launched three discrete AI methods: one for antibody design, one for protein yield optimization, and one for biomarker and goal discovery.
“Take our antibody design system for example. It’s not only a single mannequin. It’s made up of three built-in parts. First, a generative mannequin creates novel antibodies. Subsequent, predictive fashions filter these antibodies based mostly on their molecular properties. Lastly, a docking system, which makes use of physics-based mannequin, simulates the three-dimensional interactions between the antibody and its goal,” Gertz continued. The worth lies within the system as a complete, not any single mannequin, in response to the CEO. “Our prospects don’t should piece fashions collectively themselves. They get ready-to-use methods that plug straight into their workflows.”
The brand new funding comes a couple of yr and a half after the corporate raised a $5.5 million seed spherical in 2024.
Techcrunch occasion
San Francisco
|
October 13-15, 2026
Since then, the two-year-old startup has scaled rapidly. Converge has signed 40 partnerships with pharmaceutical and biotech corporations and is at the moment working about 40 packages on its platform, Gertz stated. It really works with prospects throughout the U.S., Canada, Europe and Israel and is now increasing into Asia.
The crew has additionally grown quickly, rising to 34 workers from simply 9 in November 2024. Alongside the way in which, Converge has begun publishing public case research. In a single, the startup helped a associate enhance protein yield by 4 to 4.5X in a single computational iteration. In one other, the platform generated antibodies with extraordinarily excessive binding affinity, reaching the single-nanomolar vary, Gertz famous.
AI-driven drug discovery is experiencing a surge of curiosity. Last year, Eli Lilly teamed up with Nvidia to construct what the businesses referred to as the pharma business’s strongest supercomputer for drug discovery. And in October 2024, the builders behind Google DeepMind’s AlphaFold project won a Nobel Prize in Chemistry for creating AlphaFold, the AI system that may predict protein constructions.
When requested concerning the momentum and the way it’s shaping Converge Bio’s development, Gertz stated that the corporate is witnessing the biggest monetary alternative within the historical past of life sciences and the business is shifting from “trial-and-error” approaches to data-driven molecular design.
“We really feel the momentum deeply, particularly in our inboxes. A yr and a half in the past, after we based the corporate, there was a whole lot of skepticism,” Gertz informed TechCrunch. That skepticism has vanished remarkably rapidly, because of profitable case research from corporations like Converge and from academia, he added.
Massive language fashions are gaining consideration in drug discovery for his or her potential to investigate organic sequences and recommend new molecules, however challenges like hallucinations and accuracy stay. “In textual content, hallucinations are often straightforward to identify,” the CEO stated. “In molecules, validating a novel compound can take weeks, so the fee is far increased.” To sort out this, Converge pairs generative fashions with predictive ones, filtering new molecules to scale back danger and enhance outcomes for its companions. “This filtration isn’t excellent, however it considerably reduces danger and delivers higher outcomes for our prospects,” Gertz added.
TechCrunch additionally requested about consultants like Yann LeCun, who stay skeptical about using LLMs. “I’m an enormous fan of Yann LeCun, and I utterly agree with him. We don’t depend on text-based fashions for core scientific understanding. To really perceive biology, fashions must be skilled on DNA, RNA, proteins, and small molecules,” Gertz defined.
Textual content-based LLMs are used solely as help instruments, for instance, to assist prospects navigate literature on generated molecules. “They’re not our core know-how,” Gertz stated. “We’re not tied to a single structure. We use LLMs, diffusion fashions, conventional machine studying, and statistical strategies when it is sensible.”
“Our imaginative and prescient is that each life-science group will use Converge Bio as its generative AI lab. Moist labs will all the time exist, however they’ll be paired with generative labs that create hypotheses and molecules computationally. We need to be that generative lab for the complete business,” Gertz stated.
