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Home / Business & Corporate / AI biopharma venture Enveda secures $311 million to drive clinical development of plant-derived therapies

AI biopharma venture Enveda secures $311 million to drive clinical development of plant-derived therapies

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AI biopharma venture Enveda secures $311 million to drive clinical development of plant-derived therapies
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Boulder-based biotech firm Enveda has raised $311 million in Series E funding to advance its pipeline of novel small-molecule drugs. The capital injection will accelerate late-stage clinical trials for oral therapeutics targeting inflammatory and metabolic diseases.

UNITED STATES OF AMERICA —Biotechnology venture Enveda has completed a $311 million Series E financing round aimed at scaling its proprietary drug discovery engine and expanding clinical operations. Led by Catalio Capital Management, the round drew support from prominent institutional, technology, and healthcare investors, including Durable Capital Partners, ICONIQ, Lightspeed, Surveyor Capital, and accounts advised by T. Rowe Price Investment Management. The latest transaction elevates the firm's total funding to over $845 million, supporting the progression of lead drug candidates into late-stage trials.

Founded in 2019 and headquartered in Boulder, Colorado, Enveda is a clinical-stage biotechnology enterprise that utilizes machine learning models to identify bioactive chemical structures from plant species. The company leverages automated laboratory workflows and its foundational artificial intelligence platform, PRISM, to analyze natural chemistry, predict molecular structures, and map biological functions for drug development.

The capital deployment comes following early-stage clinical trial readouts for two lead oral drug candidates targeting atopic dermatitis and metabolic health. Enveda plans to utilize the funds to initiate broader clinical studies for its lead assets, advance a third clinical candidate for inflammatory bowel disease, and further optimize its automated biological testing facilities. By leveraging naturally occurring chemical diversity, the enterprise aims to streamline early-stage compound identification and reduce overall drug development timelines.

This financing underscores growing venture capital appetite for computational biology platforms that demonstrate tangible clinical progression. Integrating predictive machine learning into small-molecule discovery enables developers to navigate complex biological datasets more efficiently than traditional high-throughput screening methods. As pharmaceutical firms face rising R&D costs and approaching patent expirations on legacy blockbusters, platform technologies that accelerate compound identification present critical strategic opportunities for global drug manufacturers and institutional healthcare investors.

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