Converge Bio Secures $25M to Revolutionize Drug Discovery with AI
In a significant advancement for the pharmaceutical and biotech sectors, Converge Bio, a startup specializing in AI-driven drug discovery, has successfully raised $25 million in an oversubscribed Series A funding round. This round was led by Bessemer Venture Partners, with additional participation from TLV Partners, Saras Capital, and Vintage Investment Partners. Notably, the funding also saw contributions from executives at leading tech companies, including Meta, OpenAI, and Wiz.
Operating out of Boston and Tel Aviv, Converge Bio is at the forefront of integrating generative AI into the drug development process. By training AI models on extensive molecular datasets encompassing DNA, RNA, and protein sequences, the company aims to expedite the traditionally lengthy and costly drug discovery pipeline.
CEO and co-founder Dov Gertz elaborated on the company’s mission, stating, 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.
Converge Bio has already introduced three specialized AI systems:
1. Antibody Design System: This system comprises three integrated components. First, a generative model creates novel antibodies. Next, predictive models filter these antibodies based on their molecular properties. Finally, a docking system simulates the three-dimensional interactions between the antibody and its target, ensuring optimal binding affinity.
2. Protein Yield Optimization System: Designed to enhance the production efficiency of proteins, this system utilizes AI to identify and implement modifications that increase yield, thereby reducing manufacturing costs and time.
3. Biomarker and Target Discovery System: This tool aids in identifying new biomarkers and therapeutic targets by analyzing complex biological data, facilitating the development of more effective and personalized treatments.
These systems are designed to seamlessly integrate into existing pharmaceutical and biotech workflows, providing ready-to-use solutions that eliminate the need for companies to develop their own AI models from scratch.
This latest funding round comes approximately 18 months after Converge Bio’s initial seed round of $5.5 million in 2024. Since then, the company has experienced rapid growth, completing over 40 programs with more than a dozen clients across the U.S., Canada, Europe, and Israel, and is now expanding into Asia.
The team has also grown significantly, increasing from nine employees in November 2024 to 34 currently. Converge Bio has begun publishing case studies showcasing its impact. In one instance, the company helped a partner boost protein yield by 4 to 4.5 times in a single computational iteration. In another, the platform generated antibodies with extremely high binding affinity, reaching the single-nanomolar range.
The AI-driven drug discovery sector is witnessing a surge of interest and investment. For example, in June 2024, Formation Bio raised $372 million to enhance drug development using AI. Similarly, in January 2025, Bioptimus secured $41 million to develop a foundational AI model for biology.
Addressing the challenges associated with large language models (LLMs) in drug discovery, Gertz acknowledged issues like hallucinations and accuracy. He emphasized the importance of training models on biological data rather than text, stating, To truly understand biology, models need to be trained on DNA, RNA, proteins, and small molecules. Converge Bio employs a combination of generative and predictive models to filter new molecules, reducing risk and improving outcomes for its partners.
Looking ahead, Gertz envisions a future where every life-science organization utilizes Converge Bio as its generative AI lab. He stated, 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.
