Isomorphic Labs, a London-based AI drug discovery company spun off from Google DeepMind, has closed a $2.1 billion funding round to accelerate development of its AI drug discovery engine, aiming for initial clinical trials by the end of 2026.
- Raised $2.1 billion in latest funding led by Thrive Capital
- Funding to scale AI-driven drug design technology
- First clinical trials now expected by late 2026
What happened
Isomorphic Labs announced that it has raised $2.1 billion in a new funding round led by Thrive Capital, with additional investments from Google Ventures, Alphabet, MGX, Temasek, and CapitalG. This round follows the company's initial $600 million raise in 2025. Founded in 2021, Isomorphic Labs emerged from Google DeepMind and focuses on AI technologies to accelerate the drug discovery process.
The company plans to use this injection of capital to expand its AI-powered drug design engine and significantly increase its research capacity. This move underscores confidence from major investors in the potential of AI to revolutionize pharmaceutical development, enabling faster and more precise creation of new therapies.
Why it matters
Isomorphic Labs represents a leading example of how artificial intelligence can be applied to solve complex challenges in healthcare, specifically drug discovery. Its technology builds on DeepMind’s breakthrough AlphaFold system, which accurately predicts protein structures, a key step in understanding diseases and creating targeted treatments.
The sizeable funding reflects a growing industry emphasis on harnessing AI to shorten drug development timelines and reduce costs. Successfully bringing AI-designed drugs to clinical trials could transform the pharmaceutical landscape, improving patient outcomes and accelerating access to effective treatments.
What to watch next
Investors and industry watchers will be closely monitoring Isomorphic Labs’ progress toward initiating clinical trials by the end of 2026, a timeline slightly adjusted from an initial goal of late 2025. The company’s ability to translate its AI platform into viable drug candidates will be critical for its long-term success and validation of AI-driven discovery.