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CASE STUDY

Enveda Biosciences: Building a Backbone for Machine Learning Increases Speed of Discovery by 230%

Enveda Case Study
Enveda logo

Using the power of nature’s chemistry to inspire new medicines for the toughest diseases

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.

Company

Number of employees: 11 – 50

Industry: Biotechnology

Location: San Francisco, CA, USA

Goal

Efficiently capture plant characterization data and structure it for machine learning.

CHALLENGES

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.

Results
  • 66%
    of scientists said they had a more cohesive view of experimental progress
  • 130%
    increase in data integrity
  • 230%
    reported average increase in speed to discovery