Converge Bio Secures $25M in Funding Led by Bessemer and Tech Leaders from Meta OpenAI and Wiz
Converge Bio has raised $25 million to enhance AI-driven drug discovery with support from major tech execs and investors.
Boston: Converge Bio is a new startup that uses AI to help drug companies create medicines faster. They just got $25 million from some big investors. The money will help them compete better in a fast-growing market for AI in drug discovery.
Converge, with offices in Boston and Tel Aviv, uses special AI models that learn from biological data helping to speed up drug development. They just finished a $25 million funding round led by Bessemer Venture Partners and got help from leaders at Meta, OpenAI, and Wiz.
Every company needs to discover new drugs over time. Converge helps with this cycle by using AI models that look at DNA, RNA, and proteins. CEO Dov Gertz says their technology can help bring new drugs to market quicker.
They have already created three systems to help companies. One system designs new antibodies while another improves protein yield. The third helps find markers for new targets.
Gertz explained that their antibody system has three parts. One creates new antibodies, a second filters them, and the third simulates how they work together. This saves companies the hassle of making their models.
Not too long ago, in 2024, Converge raised $5.5 million in an earlier funding round. Since then, they’ve grown quickly to finish over 40 projects with many drug companies around the world. They are also working on expanding to Asia.
Their team grew from 9 to 34 people in just under two years. They have shared success stories, one even showed how they increased a protein yield by 4.5 times. Another story showed how they created powerful antibodies.
AI in drug discovery has gained a lot of interest recently. Well-known companies and projects, like Eli Lilly’s supercomputer and Google’s AlphaFold, highlight AI’s role in this field.
Gertz said they see big opportunities ahead as the drug industry changes. There is much less belief in old methods now that success stories are coming in.
Many are excited about using large AI models to analyze biological data. However, Gertz mentioned that there are still issues to solve. They mix their generative models with predictive models to help reduce risks in creating new drug compounds.
Gertz admires experts who remain cautious about using text-based AI models. He believes understanding biology requires training models on specific biological data directly.
Text-based models, Gertz noted, are used only as tools for helping find literature. They are not the main technology. Converge uses many advanced methods to fit their needs.
Gertz sees a future where every life-science organization will use Converge’s AI as a core tool. Traditional labs will still exist, but they will work together with AI labs to help create new ideas and medicines.