Artificial intelligence is moving quickly into drug discovery as pharmaceutical and biotech companies look for ways to cut years off R&D timelines and increase the chances of success amid rising cost. More than 200 startups are now competing to weave AI directly into research workflows, attracting growing interest from investors. Converge Bio is the latest company to ride that shift, securing new capital as competition in the AI-driven drug discovery space heats up.
The Boston- and Tel Aviv–based startup, which helps pharma and biotech companies develop drugs faster using generative AI trained on molecular data, has raised a $25 million oversubscribed Series A round, led by Bessemer Venture Partners. TLV Partners, Saras Capital and Vintage Investment Partners also joined the round, along with additional backing from unidentified executives at Meta, OpenAI, and Wiz.
Read More – Locus Biosciences Receives $3.3M Award From NIH
In practice, Converge trains generative models on DNA, RNA, and protein sequences then plugs them into pharma and biotech’s workflows to speed up drug development.
“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,” Converge Bio CEO and co-founder Dov Gertz said in an exclusive interview with TechCrunch. “Our platform continues to expand across these stages, helping bring new drugs to market faster.”
So far, Converge has rolled out customer-facing systems. The startup has already introduced three discrete AI systems: one for antibody design, one for protein yield optimization, and one for biomarker and target discovery.
“Take our antibody design system as an example. It’s not just a single model. It’s made up of three integrated components. First, a generative model creates novel antibodies. Next, predictive models filter those antibodies based on their molecular properties. Finally, a docking system, which uses physics-based model, simulates the three-dimensional interactions between the antibody and its target,” Gertz continued. The value lies in the system as a whole, not any single model, according to the CEO. “Our customers don’t have to piece models together themselves. They get ready-to-use systems that plug directly into their workflows.”
Read More – Nanochon Closes $4.1M Seed Prime II Funding Round
