Artificial intelligence is rapidly evolving into drug discovery as pharmaceutical and biotechnology companies look for ways to shave years off R&D timelines and increase the chance of success amid rising costs. More more than 200 startups are now competing to integrate AI directly into research streams, attracting growing interest from investors. Converge Organic is the latest company to make this shift, securing new capital as competition in the field of AI-driven drug discovery heats up.
The Boston and Tel Aviv-based startup, which helps pharmaceutical and biotech companies develop drugs faster with generative AI trained on molecular data, has raised an oversubscribed $25 million Series A, led by Bessemer Venture Partners. TLV Partners, Saras Capital and Vintage Investment Partners also joined the round, with additional support from unidentified executives from Meta, OpenAI and Wiz.
In practice, Converge trains generative models on DNA, RNA, and protein sequences, then connects them to pharmaceutical and biotechnology workflows to accelerate drug development.
“The drug development life cycle has defined stages – from target identification and discovery through manufacturing, clinical trials and beyond – and within each of them, there are experiments that we can support,” said Dov Gertz, CEO and co-founder of Converge Bio, in an exclusive interview with TechCrunch. “Our platform continues to develop through these stages, helping to accelerate the commercialization of new medicines. »
So far, Converge has deployed 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 target discovery.
“Let’s take our antibody design system as an example. It’s not just a single model. It’s made up of three integrated components. First, a generative model creates new antibodies. Then, 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 a single model, according to the CEO. “Our customers don’t need to piece together the models themselves. They get ready-to-use systems that plug directly into their workflows.”
The new funding comes 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 seen rapid growth. Converge has completed more than 40 programs with more than a dozen pharmaceutical and biotechnology clients, Gertz said. It works with clients in the United States, Canada, Europe and Israel and is now expanding into Asia.
The team also grew rapidly, growing from nine employees to 34 by November 2024. At the same time, Converge began 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 case, the platform generated antibodies with extremely high binding affinity, reaching the nanomolar range, Gertz noted.
AI-based drug discovery is seeing renewed interest. Last yearEli Lilly has teamed up with Nvidia to build what the companies call 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 to create AlphaFold, the AI system capable of predicting protein structures.
Asked about this dynamic and how it is shaping Converge Bio’s growth, Gertz said the company is witnessing the largest financial opportunity in the history of life sciences and that the industry is moving from “trial and error” approaches to data-driven molecular design.
“We feel this dynamic 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. That skepticism disappeared remarkably quickly, thanks to successful case studies from companies like Converge and academia, he added.
Large language models are attracting attention in drug discovery because of their ability to analyze biological sequences and suggest new molecules, but challenges such as hallucinations and accuracy remain. “In texts, 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 solve this problem, Converge combines generative models with predictive models, filtering new molecules to reduce risks and improve outcomes for its partners. “This filtration is not perfect, but it significantly reduces risk and provides better results for our customers,” added Gertz.
TechCrunch also interviewed experts like Yann LeCun, who remain skeptical about the use of LLMs. “I’m a big fan of Yann LeCun and completely agree with him. We don’t rely on textual 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 support tools, for example to help customers navigate the literature on generated molecules. “They are not our core technology,” Gertz said. “We are not tied to a single architecture. We use LLMs, diffusion models, traditional machine learning and statistical methods where it makes sense.”
“Our vision is that every life sciences organization will use Converge Bio as a generative AI lab. Wet labs will still exist, but they will be paired with generative labs that create hypotheses and molecules computationally. We want to be that generative lab for the entire industry,” Gertz said.
The article has been updated to include information on the number of customers.
