Alphabet's Isomorphic Labs, founded by DeepMind co-founder Demis Hassabis, closed a $2.1 billion Series B round led by Thrive Capital. The funding will accelerate development of its IsoDDE platform and push drug candidates toward clinical trials.
Isomorphic Labs is scaling its artificial intelligence platform designed to accelerate drug discovery. The Series B funding represents a major milestone for the company, which emerged from Alphabet's DeepMind division to commercialize AI-driven pharmaceutical research.
The company will use the capital to expand its IsoDDE (Isomorphic Drug Discovery Engine) platform, which applies machine learning to molecular design and drug candidate identification. The funding also supports advancing multiple drug programs toward human clinical trials, marking a shift from research into development.
Hassabis leads the company alongside a team combining expertise in AI, chemistry, and biology. Isomorphic Labs operates as a separate entity under Alphabet but maintains ties to DeepMind's research capabilities.
AI drug discovery has attracted significant investment as researchers demonstrate potential to compress development timelines and reduce costs. Early applications have shown promise in identifying novel compounds and predicting molecular properties that traditionally required extensive laboratory work.
Isomorphic Labs competes in a growing sector that includes other AI-focused biotech companies and traditional pharmaceutical firms integrating machine learning into their pipelines. The move toward clinical trials indicates the company believes its AI-discovered compounds are viable candidates for human testing.
Thrive Capital's participation suggests confidence in both the technology and the team's ability to execute. Other investors in the round were not disclosed.
The funding round arrives as Alphabet continues investing in healthcare-adjacent ventures through its various subsidiaries and research initiatives. Isomorphic's progress will test whether AI can meaningfully impact drug discovery timelines and success rates at scale.
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