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.