Every sector of the global economy is racing to integrate Artificial Intelligence to automate functions, boost productivity, and accelerate growth. Thus, the health sector has been deepening its integration as the results trickle in.
AI is moving decisively from the margins to the core of drug discovery, as pharmaceutical and biotech companies seek to shave years off development timelines, lower soaring research costs, and improve the odds of success in an industry defined by high failure rates.
More than 200 startups are now competing to embed AI directly into research and development workflows, and investor appetite for the sector continues to build. Converge Bio has emerged as the latest beneficiary of that momentum, securing fresh capital as competition in AI-driven drug discovery intensifies.
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The Boston- and Tel Aviv–based startup announced a $25 million oversubscribed Series A round led by Bessemer Venture Partners, with participation from TLV Partners and Vintage Investment Partners. The round also included backing from several undisclosed senior executives affiliated with Meta, OpenAI, and Wiz, underscoring the growing convergence between frontier AI talent and life sciences.
According to TechCrunch, the raise comes about 18 months after Converge closed a $5.5 million seed round in 2024, marking a rapid escalation in funding as the company scales its technology and customer base.
Converge Bio focuses on using generative AI trained directly on molecular data — including DNA, RNA, and protein sequences — to help pharmaceutical and biotech companies accelerate drug development. Rather than positioning itself as a standalone research tool, the company integrates its models into existing industry workflows, targeting multiple stages of the drug-development lifecycle, from early discovery through optimization and manufacturing.
“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,” CEO and co-founder Dov Gertz said. “Our platform continues to expand across these stages, helping bring new drugs to market faster.”
A key differentiator for Converge is its emphasis on complete, production-ready systems rather than isolated models. The company has already rolled out three customer-facing AI systems: one for antibody design, one for protein yield optimization, and one for biomarker and target discovery. Each system combines multiple layers of AI and physics-based modeling to reduce risk and improve reliability.
Gertz described the antibody design system as a case in point. A generative model first creates novel antibody candidates. Predictive models then screen those candidates based on molecular properties such as stability and binding potential. Finally, a docking system based on physics simulations evaluates three-dimensional interactions between the antibody and its target.
“It’s not a single model,” Gertz said. “The value is in the system as a whole. Our customers don’t have to piece models together themselves — they get ready-to-use systems that plug directly into their workflows.”
Despite being just two years old, Converge has scaled quickly. The company has signed roughly 40 partnerships with pharmaceutical and biotech firms and is currently running about 40 active programs on its platform. Its customers span the U.S., Canada, Europe, and Israel, and the company is now expanding into Asia. Internally, Converge has grown from nine employees in November 2024 to 34 today, reflecting both technical and commercial expansion.
The startup has also begun publishing case studies to demonstrate real-world impact, a critical step in an industry that has long been cautious about AI claims. In one case, Converge helped a partner increase protein yield by four to 4.5 times in a single computational iteration. In another, its platform generated antibodies with extremely high binding affinity, reaching the single-nanomolar range — a benchmark that signals strong therapeutic potential.
The funding round lands amid a broader surge of interest in AI-driven drug discovery. Major pharmaceutical companies are committing significant resources to the space. Last year, Eli Lilly partnered with Nvidia to build what the companies described as the pharma industry’s most powerful supercomputer for drug discovery. In October 2024, the creators of Google DeepMind’s AlphaFold won the Nobel Prize in Chemistry, a watershed moment that cemented AI’s scientific credibility by recognizing its ability to predict protein structures with unprecedented accuracy.
Gertz said the shift in industry sentiment has been swift and striking. “When we founded the company, there was a lot of skepticism,” he said. “That skepticism has vanished remarkably quickly.”
He described the current moment as a fundamental transition away from traditional trial-and-error experimentation toward data-driven molecular design, calling it the largest financial opportunity in the history of life sciences.
But there are challenges. Generative models can hallucinate — a manageable issue in text applications but a costly one in biology, where validating a novel molecule can take weeks or months. Converge addresses this by pairing generative models with predictive filters that narrow down candidates before they reach the lab.
“This filtration isn’t perfect,” Gertz said, “but it significantly reduces risk and delivers better outcomes for our customers.”
The company is also selective about how it uses large language models. While text-based LLMs can assist with tasks like navigating scientific literature, Gertz said they are not suitable as the foundation for biological understanding. He echoed the skepticism of figures such as Yann LeCun, arguing that meaningful progress in biology requires models trained directly on molecular data rather than text alone.
Converge’s platform blends multiple approaches, including LLMs, diffusion models, traditional machine learning, and statistical methods, depending on the problem at hand.
Looking ahead, Converge Bio’s ambition extends beyond individual tools or use cases. Gertz envisions a future where every life-science organization operates a “generative lab” alongside its wet lab, using AI to generate hypotheses and molecular candidates before moving into physical experimentation.
“Wet labs will always exist,” he said. “But they’ll be paired with generative labs that create hypotheses and molecules computationally. We want to be that generative lab for the entire industry.”
As competition intensifies and capital continues to flow into AI-driven drug discovery, Converge Bio’s rapid growth highlights how quickly the field is maturing. The ultimate test, however, will be whether these systems can consistently translate computational promise into approved therapies.