Artificial intelligence is rapidly advancing in drug discovery as pharmaceutical and biotech companies look for ways to cut years off R&D timelines and increase the chances of success amid rising costs. More more than 200 startups There is now competition to incorporate AI directly into the research workflow, which is attracting increasing interest from investors. convergence bio is the latest company to drive that change, raising new capital as competition increases in the AI-powered drug discovery field.
The Boston- and Tel Aviv-based startup, which helps pharma and biotech companies develop medicines faster using generic AI trained on molecular data, has raised an oversubscribed $25 million Series A round led by Bessemer Venture Partners. TLV Partners and Vintage Investment Partners also joined the round, with additional support from undisclosed executives from Meta, OpenAI, and Viz.
In practice, Converge trains generative models on DNA, RNA and protein sequences and then plugs them into pharma and biotech’s workflows to speed up 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,” Dov Gertz, CEO and co-founder of Converge Bio, said in an exclusive interview with TechCrunch. “Our platform continues to expand at these stages, helping to bring new medicines to market faster.”
So far, Converge has introduced customer-facing systems. The startup has already introduced three separate AI systems: one for antibody design, one for protein yield optimization, and one for biomarker and target discovery.
“Take our antibody design system as an example. It’s not just a model. It’s composed 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 models, simulates the three-dimensional interactions between the antibody and its target,” Gertz continued. According to the CEO, the value lies in the entire system, not in any one model. “Our customers don’t have to piece together models themselves. They get ready-to-use systems that plug directly into their workflow.”
The new funding comes about a year and a half after the company raised a seed round of $5.5 million in 2024.
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Since then, the two-year-old startup has grown rapidly. Gertz said Converge has signed 40 partnerships with pharmaceutical and biotech companies and is currently running about 40 programs on its platform.. It works with clients in the US, Canada, Europe and Israel and is now expanding into Asia.
The team has also grown rapidly, from just nine to 34 employees in November 2024. Additionally, Converge has begun publishing public case studies. In one, the startup helped a partner increase protein yield by 4 to 4.5X in a single computational iteration. In another, the platform generated antibodies with extremely high binding affinities, reaching the single-nanomolar range, Gertz noted.
There is growing interest in AI-powered drug discovery. last yearEli Lilly has teamed up with Nvidia to create the pharma industry’s most powerful supercomputer for drug discovery. And in October 2024, the developers were left behind Google DeepMind’s AlphaFold project wins Nobel Prize In chemistry, AI systems that can predict protein structures are used to create alphafolds.
Asked about the momentum and how it is shaping Converge Bio’s growth, Gertz said the company is seeing the largest financial opportunity in the history of life sciences and that the industry is shifting from a “trial-and-error” approach to data-driven molecular design.
“We feel the momentum deeply, especially in our inboxes. A year and a half ago, when we founded the company, there was a lot of skepticism,” Gertz told TechCrunch. Thanks to companies like Converge and successful case studies from academia, this skepticism is rapidly disappearing, he said.
Large language models are gaining attention in drug discovery due to their ability to analyze biological sequences and suggest new molecules, but challenges such as hallucinogenicity and accuracy remain. “In text, hallucinations are usually easy to recognize,” the CEO said. “In molecules, it can take several weeks to validate a new compound, so the costs are very high.” To address this, Converge has combined generative models with predictive models, filtering out new molecules to reduce risk and improve outcomes for its partners. “This filtration is not perfect, but it significantly reduces risk and provides better outcomes for our customers,” Gertz said.
TechCrunch also asked experts like Yann LeCun, who remains There is doubt about the use of LLM. “I’m a big fan of Yann Lacan, and I completely agree with him. We don’t rely on text-based models for basic scientific understanding. To truly understand biology, models need to be trained on DNA, RNA, proteins, and small molecules,” Gertz explained.
Text-based LLMs are used only as a support tool, for example, to help clients navigate the literature on generated molecules. “They’re not our core technology,” Gertz said. “We are 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-sciences organization will use Converge Bio as their generative AI lab. Wet labs will always exist, but they will be paired with generative labs that computationally create hypotheses and molecules. We want to be the generative lab for the entire industry,” Gertz said.
