$25M Game-Changer: How Ex-Meta & OpenAI Execs Are Disrupting Bio Tech—Don’t Miss Out!

In an era where the costs and timelines of drug development continue to escalate, artificial intelligence (AI) is emerging as a transformative force in the pharmaceutical and biotech industries. More than 200 startups are now competing to integrate AI into research workflows, seeking to cut years off research and development (R&D) timelines and enhance the likelihood of successful outcomes. Among these is Converge Bio, a Boston- and Tel Aviv-based startup that recently secured $25 million in an oversubscribed Series A round led by Bessemer Venture Partners, with participation from TLV Partners and Vintage Investment Partners, along with backing from unidentified executives at Meta, OpenAI, and Wiz.

Converge Bio specializes in employing generative AI that has been trained on molecular data to expedite drug development. In practice, the startup focuses on training generative models on sequences of DNA, RNA, and proteins. These models are then integrated into the workflows of pharmaceutical and biotech firms to streamline various stages of the drug-development lifecycle. As CEO and co-founder Dov Gertz explained in an exclusive interview, “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. Our platform continues to expand across these stages, helping bring new drugs to market faster.”

So far, Converge has launched customer-facing systems, introducing three distinct AI solutions: one for antibody design, one for protein yield optimization, and another for biomarker and target discovery. Gertz elaborated on the antibody design system, noting that it consists of three integrated components: a generative model to create novel antibodies, predictive models to filter these antibodies based on molecular properties, and a physics-based docking system that simulates the interactions between the antibody and its target. This integrated approach allows customers to access ready-to-use systems that seamlessly fit into their existing workflows.

The recent funding marks a significant milestone for Converge, coming approximately a year and a half after it raised $5.5 million in its seed round in 2024. Since then, the two-year-old startup has rapidly expanded, having signed 40 partnerships with pharmaceutical and biotech companies and currently managing around 40 programs on its platform. Gertz mentioned that Converge is working with customers across the U.S., Canada, Europe, and Israel, with plans to expand into Asia.

As the company grows, its workforce has also increased significantly, from just nine employees in November 2024 to 34 today. Converge has begun publishing public case studies to showcase its impact; one highlighted a partnership that increased protein yield by 4 to 4.5 times in a single computational iteration, while another demonstrated the platform's ability to generate antibodies with notably high binding affinity, achieving levels in the single-nanomolar range.

The momentum around AI-driven drug discovery is palpable. In 2024, Eli Lilly announced a collaboration with Nvidia to develop what they termed the pharma industry's most powerful supercomputer for drug discovery. Moreover, the developers behind Google DeepMind's AlphaFold project were awarded the Nobel Prize in Chemistry for their AI system that predicts protein structures. Gertz noted that the shifting landscape from "trial-and-error" methods to data-driven molecular design represents the largest financial opportunity in the history of life sciences.

“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. He pointed out that this skepticism has largely dissipated, thanks to successful case studies from companies like Converge and academic research.

Despite the advances, challenges remain, particularly concerning large language models (LLMs) used in drug discovery. While these models can analyze biological sequences and suggest new molecules, issues like hallucinations and accuracy linger. Gertz explained, “In text, hallucinations are usually easy to spot. In molecules, validating a novel compound can take weeks, so the cost is much higher.” To mitigate this, Converge pairs generative models with predictive ones, which helps filter new molecules to reduce risk and improve outcomes for its partners.

In light of skepticism from experts like Yann LeCun regarding the reliance on LLMs, Gertz defended Converge's approach. “We don’t rely on text-based models for core scientific understanding. To truly understand biology, models need to be trained on DNA, RNA, proteins, and small molecules,” he emphasized. Text-based LLMs serve only as support tools, assisting customers in navigating literature on generated molecules, but are not integral to the core technology.

Looking ahead, Gertz shared a bold vision: “Our vision is that every life-science organization will use Converge Bio as its generative AI lab. Wet labs will always exist, 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 AI continues to reshape drug discovery, companies like Converge Bio are positioning themselves at the forefront of this pivotal shift, aiming to accelerate the delivery of life-saving drugs to the market.