Artificial intelligence is rapidly moving into drug discovery as pharmaceutical and biotech companies look for ways to cut R&D timelines by years and increase the likelihood of success amid rising costs. More from 200 starters now competing to weave AI directly into research workflows, attracting investors. Convergent Bio is the latest company to ride that shift, securing new capital as competition in the AI-driven drug discovery space heats up.
The Boston- and Tel Aviv-based startup, which helps pharmaceutical and biotech companies develop drugs faster using generative AI trained on molecular data, has raised an oversubscribed Series A round of $25 million, led by Bessemer Venture Partners. TLV Partners and Vintage Investment Partners also participated in the round, along with additional support from undisclosed executives at Meta, OpenAI, and Wiz.
In practice, Converge trains generative models on DNA, RNA, and protein sequences and then connects them to pharmaceutical and biotech workflows to accelerate drug development.
“The life cycle of drug development has defined stages – from target identification and discovery to manufacturing, clinical trials, and more – and in each, there are experiments that we can support,” said Converge Bio CEO and co-founder Dov Gertz in an exclusive interview with TechCrunch. “Our platform continues to scale these stages, helping to bring new drugs to market faster.”
So far, Converge has launched a customer-facing system. The startup has introduced three discrete AI systems: one for antibody design, one for protein yield optimization, and one for biomarker and target discovery.
“An example of our antibody design system. It is not just one model. It consists of three integrated components. First, the generative model creates a novel antibody. Next, the predictive model refines the antibody based on its molecular properties. Finally, the docking system, which uses a physics model, simulates the three-dimensional interaction between the antibody and the target,” continued Gertz. The value lies in the system as a whole, not a single model, according to the CEO. “Our customers don’t have to put together their own models. They get a ready-to-use system that connects directly to their workflow.”
The new funding comes about a year and a half after the company raised a $5.5 million in seed round in 2024.
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Since then, the beginning of the two-year-old has been very fast. Converge has signed 40 partnerships with pharmaceutical and biotech companies and currently runs about 40 programs on its platform, Gertz said.. It works with customers in the US, Canada, Europe and Israel and is now expanding into Asia.
The team is also growing rapidly, growing to 34 employees from just nine in November 2024. Along the way, Converge has begun publishing public case studies. In turn, startups help partners boost protein yield by 4 to 4.5X in a single computational iteration. In another, the platform produces antibodies with very high binding affinity, reaching the single nanomolar range, Gertz noted.
AI-powered drug discovery is experiencing a surge of interest. last yearEli Lilly is teaming up with Nvidia to build what the company says is the pharmaceutical industry’s most powerful supercomputer for drug discovery. And in October 2024, the developer behind Google DeepMind’s AlphaFold project won the Nobel Prize in Chemistry to create AlphaFold, an AI system that can predict protein structures.
When asked about momentum and how Converge Bio’s growth is shaping up, Gertz said the company is witnessing the biggest financial opportunity in life sciences history 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 inbox. A year and a half ago, when we founded the company, there was a lot of skepticism,” said Gertz TechCrunch. That skepticism is quickly disappearing, thanks to successful case studies from companies like Converge and from academia, he added.
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 hallucinations and accuracy remain. “In text, hallucinations are usually easy to spot,” says the CEO. “In molecular, the validation of novel compounds can take weeks, so the cost is higher.” To address this, Converge pairs generative models with predictive, refining new molecules to reduce risk and improve outcomes for partners. “This filtration is not perfect, but it significantly reduces risk and provides better results for customers,” added Gertz.
TechCrunch also asked about experts like Yann LeCun, who stayed skeptical about using LLMs. “I am a huge fan of Yann LeCun, and I completely agree with him. We do not rely on text-based models for core scientific understanding. To truly understand biology, models must be trained on DNA, RNA, proteins, and small molecules,” explained Gertz.
The text-based LLM is used only as a support tool, for example to help customers navigate the literature on the molecules they are creating. “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 they are right.”
“Our vision is that every life sciences organization will use Converge Bio as a generative AI lab. The wet lab will always be there, but it will be paired with a generative lab that creates hypotheses and molecules computationally. We want to be a generative lab for all industries,” said Gertz.