Enveda Biosciences

Building a backbone for machine learning increases speed of discovery by 230%

Efficiently capture plant characterization data and structure it for machine learning

Enveda’s core technology is a computational metabolomics platform, which works like a powerful chemical search engine to unearth millions of new chemicals from mass spectral data, link them to activity in preclinical assays, and inspire drug-like modifications at scale. They are using this technology to create a diverse range of chemical libraries to target hitherto undruggable disease mechanisms and “reverse translate” active leads in long-used medicinal plants into successful drugs.



of scientists said they had a more cohesive view of experimental progress


increase in data integrity


reported average increase in speed to discovery


Data scattered across different systems

Researchers lacked a central solution for experiment tracking and data capture, making it difficult for them to obtain a cohesive view of experimental progress and results.

Lack of data standardization

Enveda needed clean, standardized data to flow from the bench to their digital systems effortlessly, so they could feed their machine learning models at scale.

Need for a scalable informatics solution

Enveda needed a platform that would grow with them as their data production continued to increase exponentially, while providing functionality they’ll need in the future, such as easily configurable workflows and barcoding.

Powering breakthroughs for over 1,200 biotechnology companies, from startups to Fortune 500s

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