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 costs. More than 200 startups<\/a> are now competing to weave AI directly into research workflows, attracting growing interest from investors. Converge Bio<\/a> is the latest company to ride that shift, securing new capital as competition in the AI-driven drug discovery space heats up.<\/p>\n 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.<\/p>\n In practice, Converge trains generative models on DNA, RNA, and protein sequences, then plugs them into pharma and biotech\u2019s workflows to speed up drug development.<\/p>\n \u201cThe drug-development lifecycle has defined stages \u2014 from target identification and discovery to manufacturing, clinical trials, and beyond \u2014 and within each, there are experiments we can support,\u201d Converge Bio CEO and co-founder Dov Gertz said in an exclusive interview with TechCrunch. \u201cOur platform continues to expand across these stages, helping bring new drugs to market faster.\u201d<\/p>\n 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.<\/p>\n \u201cTake our antibody design system as an example. It\u2019s not just a single model. It\u2019s 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 a physics-based model, simulates the three-dimensional interactions between the antibody and its target,\u201d Gertz continued. The value lies in the system as a whole, not any single model, according to the CEO. \u201cOur customers don\u2019t have to piece models together themselves. They get ready-to-use systems that plug directly into their workflows.\u201d<\/p>\n The new funding comes about a year and a half after the company raised a $5.5 million seed<\/a> round in 2024. \u00a0<\/p>\n Techcrunch event<\/p>\n \n\t\t\t\t\t\t\t\t\tSan Francisco<\/span> Since then, the two-year-old startup has scaled quickly. Converge has completed over 40 programs with more than a dozen pharmaceutical and biotech customers, Gertz said. It works with customers across the U.S., Canada, Europe, and Israel and is now expanding into Asia.<\/p>\n The team has also grown rapidly, increasing to 34 employees from just nine in November 2024. Along the way, Converge has begun publishing public case studies. In one, the startup helped a partner boost protein yield by 4 to 4.5X in a single computational iteration. In another, the platform generated antibodies with extremely high binding affinity, reaching the single-nanomolar range, Gertz noted.<\/p>\n AI-driven drug discovery is experiencing a surge of interest. Last year<\/a>, Eli Lilly teamed up with Nvidia to build what the companies called the pharma industry\u2019s most powerful supercomputer for drug discovery. And in October 2024, the developers behind Google DeepMind\u2019s AlphaFold project won a Nobel Prize<\/a> in Chemistry for creating AlphaFold, the AI system that can predict protein structures.<\/p>\n When asked about the momentum and how it is shaping Converge Bio\u2019s growth, Gertz said that the company is witnessing the largest financial opportunity in the history of life sciences and the industry is shifting from \u201ctrial-and-error\u201d approaches to data-driven molecular design.<\/p>\n \u201cWe 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,\u201d Gertz told TechCrunch. That skepticism has vanished remarkably quickly, thanks to successful case studies from companies like Converge and from academia, he added.<\/p>\n Large language models are gaining attention in drug discovery for their ability to analyze biological sequences and suggest new molecules, but challenges like hallucinations and accuracy remain. \u201cIn text, hallucinations are usually easy to spot,\u201d the CEO said. \u201cIn molecules, validating a novel compound can take weeks, so the cost is much higher.\u201d To tackle this, Converge pairs generative models with predictive ones, filtering new molecules to reduce risk and improve outcomes for its partners. \u201cThis filtration isn\u2019t perfect, but it significantly reduces risk and delivers better outcomes for our customers,\u201d Gertz added.<\/p>\n
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\n\t\t\t\t\t\t\t\t\t\t\t\t\tOctober 13-15, 2026<\/span>\n\t\t\t\t\t\t\t<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n![\"\"](\"https:\/\/techcrunch.com\/wp-content\/uploads\/2026\/01\/Converge-Bio-9976.jpg?w=680\")