Artificial intelligence is advancing rapidly in drug discovery as pharmaceutical and biotech companies look for ways to shave years off R&D timelines and increase the chances of success amid rising costs. More more than 200 startups they are now racing to integrate AI directly into research workflows, attracting increasing interest from investors. Converge Bio is the latest company to make that shift, securing new capital as competition in the AI-based drug discovery space heats up.
The Boston- and Tel Aviv-based startup, which helps pharmaceutical and biotech companies develop drugs faster using genetic artificial intelligence trained on molecular data, has raised a $25 million Series A oversubscription round led by Bessemer Venture Partners. TLV Partners and Vintage Investment Partners also participated in the round, along with additional backing from undisclosed executives from Meta, OpenAI and Wiz.
In practice, Converge trains genetic models on DNA, RNA and protein sequences and then plugs them into drug and biotech workflows to accelerate drug development.
“The drug development lifecycle has defined 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 said in an exclusive interview with TechCrunch. “Our platform continues to expand at these stages, helping bring new drugs to market faster.”
So far, Converge has developed 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.
“Take our antibody design system for example. It’s not just a model. It consists of three integrated components. First, a genetic model creates new antibodies. Then predictive models filter those antibodies based on their molecular properties. Finally, a docking system, which uses the physics-based model, simulates the model between the three antibodies and the Gerer targets. The value is in the system as a whole, not in any one model, according to the CEO. “Our customers don’t have to put together models themselves. They get ready-to-use systems that plug right into their workflows.”
The new funding comes about a year and a half after the company raised $5.5 million in 2024.
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Since then, the two-year-old startup has scaled rapidly. Converge has signed 40 partnerships with pharmaceutical and biotech companies and currently runs about 40 programs on its platform, Gertz said. It works with clients in the US, Canada, Europe and Israel and is now expanding into Asia.
The team has also grown rapidly, growing to 34 employees from just nine in November 2024. Along the way, Converge has begun publishing public case studies. In one, the startup helped a collaborator increase protein yield by 4- to 4.5-fold in a single computational iteration. In another, the platform produced antibodies with extremely high binding affinity, reaching the single-nanomolecular range, Gertz noted.
AI-based drug discovery is seeing a surge of interest. Last yearEli Lilly partnered with Nvidia to build what the companies called the pharmaceutical industry’s most powerful supercomputer for drug discovery. And in October 2024, the developers back Google DeepMind’s AlphaFold project won the Nobel Prize in Chemistry to create AlphaFold, the AI system that can predict protein structures.
When asked about the momentum and how it is shaping Converge Bio’s growth, Gertz said the company is aware of the largest financial opportunity in the history of the life sciences and that the industry is shifting from “trial and error” approaches to data-driven molecular design.
“We feel the momentum deeply, especially in our inbox. A year and a half ago, when we founded the company, there was a lot of skepticism,” Gertz told TechCrunch. That skepticism disappeared extremely quickly, thanks to successful case studies from companies like Converge and from academia, he added.
Large language models are gaining attention in drug discovery for their ability to analyze biological sequences and suggest new molecules, but challenges such as hallucinations and accuracy remain. “In text, hallucinations are usually easy to spot,” the CEO said. “In molecules, validating a new compound can take weeks, so the cost is much higher.” To address this, Converge combines production models with predictive, filtering new molecules to reduce risk and improve outcomes for its partners. “This filtering is not perfect, but it significantly reduces risk and provides better outcomes for our customers,” added Gertz.
TechCrunch also asked about experts like Yann LeCun, who remain skeptical about using LLM. “I’m a big fan of Yann LeCun and I totally agree with him. We don’t rely on text-based models for basic scientific understanding. To really understand biology, models need to be trained on DNA, RNA, proteins and small molecules,” explained Gertz.
Text-based LLMs are only used as support tools, for example, to help customers navigate the literature on generated molecules. “It’s not our core technology,” Gertz said. “We’re not tied to a single architecture. We use LLM, diffusion models, traditional machine learning and statistical methods when it makes sense.”
“Our vision is that every life science organization will use Converge Bio as their production AI lab. Liquid labs will always exist, but they will be combined with production labs that generate hypotheses and molecules computationally. We want to be that production lab for the entire industry,” Gertz said.
