Converge Bio Secures $25 Million Funding to Accelerate AI-Driven Drug Discovery
Converge Bio, a Boston and Tel Aviv-based startup specializing in generative AI for drug development, has successfully raised $25 million in an oversubscribed Series A funding round, primarily led by Bessemer Venture Partners. The investment also received contributions from TLV Partners, Vintage Investment Partners, and undisclosed executives from Meta, OpenAI, and Wiz. This latest funding round underscores the escalating competition as over 200 startups vie for dominance in the AI-enhanced drug discovery sector, driven by the urgent need for pharmaceutical companies to shorten research and development timelines while mitigating costs.
The firm employs advanced generative models trained on molecular data, including DNA, RNA, and protein sequences, to seamlessly integrate into existing workflows for accelerated drug development. Converge Bio has already implemented three distinct AI systems: one focused on antibody design, another on optimizing protein yields, and a third dedicated to biomarker and target discovery. As CEO Gertz explains, their antibody design system comprises three cohesive components, integrating generative modeling, predictive filtering based on molecular properties, and physics-based simulations for three-dimensional interaction analyses. This unified approach enables clients to quickly access ready-to-use systems, eliminating the need for piecemeal model integration.
Since its seed round of $5.5 million in early 2024, Converge has experienced rapid growth, establishing 40 strategic partnerships with pharmaceutical and biotech firms and concurrently engaging in approximately 40 active projects. The company is broadening its reach beyond North America and Europe into Asia, while its workforce expanded from nine to 34 employees within less than a year.
Alongside evolving its partnerships, Converge has begun to publish case studies, illustrating successes such as increased protein yields and the creation of high-affinity antibodies. This reflects a broader momentum in the AI-driven drug discovery landscape; major collaborations, such as Eli Lilly’s venture with Nvidia, have emerged to tackle the industry’s rigorous demands.
Addressing the challenges of ensuring accuracy in drug design, Gertz notes that while large language models can assist in biological sequence analysis, they are complemented by additional predictive models to reduce risks associated with new compounds. “Though not flawless, this dual-model approach significantly enhances outcomes for our clients,” he affirmed.
Converge Bio envisions a future in which every life sciences organization will utilize its platform as a generative AI lab, aiding the industry’s transition from traditional trial-and-error methods to innovative, data-driven molecular design.
