Case Studies Building a Backbone for Machine Learning Increases Speed of Discovery by 230%

Edit This Case Study Record

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

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

Technology Category	Analytics & Modeling - Machine Learning Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries	Healthcare & Hospitals Life Sciences
Applicable Functions	Product Research & Development Quality Assurance
Use Cases	Machine Condition Monitoring Predictive Maintenance
Services	Data Science Services System Integration
Challenge	Enveda Biosciences, a company that uses a computational metabolomics platform to discover new chemicals for drug development, was facing challenges in organizing and scaling their foundational data. The company was generating a large amount of structure-activity relationship (SAR), biomarker, and mechanistic readout data that they could no longer manage with siloed data solutions. They needed a data platform that could automatically structure experimental data and feed it into their machine learning pipelines. The platform also needed to be intuitive and user-friendly, as well as capable of handling robust and iterative data models customized to Enveda’s use case. Read More
About Customer	Enveda Biosciences is a company that uses the power of nature’s chemistry to inspire new medicines for the toughest diseases. Their 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. Read More
Solution	Enveda Biosciences chose Benchling as their data platform solution. Benchling's user-friendly interface and high adoption rates made it an ideal choice for Enveda. The platform also offered a user-configurable data model that could handle the high volume of multi-dimensional data that Enveda needed to process. Benchling's solution was designed to scale, which meant that Enveda could rely on it as their data production increased exponentially. The platform also offered solutions for process managers and larger teams, making it a future-proof choice for Enveda. Benchling's centralized data storage replaced disconnected Google Slides and Excel sheets, providing Enveda with a centralized source of truth. The platform's custom data model allowed lab results to be piped directly into machine learning models, saving scientists from time-consuming data cleaning. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Platform as a Service (PaaS) - Data Management Platforms

Applicable Industries

Healthcare & Hospitals

Life Sciences

Applicable Functions

Product Research & Development

Quality Assurance

Use Cases

Machine Condition Monitoring

Predictive Maintenance

Services

Data Science Services

System Integration

Challenge

Enveda Biosciences, a company that uses a computational metabolomics platform to discover new chemicals for drug development, was facing challenges in organizing and scaling their foundational data. The company was generating a large amount of structure-activity relationship (SAR), biomarker, and mechanistic readout data that they could no longer manage with siloed data solutions. They needed a data platform that could automatically structure experimental data and feed it into their machine learning pipelines. The platform also needed to be intuitive and user-friendly, as well as capable of handling robust and iterative data models customized to Enveda’s use case.

About Customer

Enveda Biosciences is a company that uses the power of nature’s chemistry to inspire new medicines for the toughest diseases. Their 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.

Solution

Enveda Biosciences chose Benchling as their data platform solution. Benchling's user-friendly interface and high adoption rates made it an ideal choice for Enveda. The platform also offered a user-configurable data model that could handle the high volume of multi-dimensional data that Enveda needed to process. Benchling's solution was designed to scale, which meant that Enveda could rely on it as their data production increased exponentially. The platform also offered solutions for process managers and larger teams, making it a future-proof choice for Enveda. Benchling's centralized data storage replaced disconnected Google Slides and Excel sheets, providing Enveda with a centralized source of truth. The platform's custom data model allowed lab results to be piped directly into machine learning models, saving scientists from time-consuming data cleaning.

Impact #1	Enveda's team achieved a 100-percent adoption rate within just two months of transitioning to Benchling.
Impact #2	The speed of experiments doubled, driving crucial momentum for Enveda’s official launch in December 2020.
Impact #3	Benchling's solution is designed to scale, which means Enveda won’t have to worry about outgrowing their data foundation.

Impact #1

Enveda's team achieved a 100-percent adoption rate within just two months of transitioning to Benchling.

Impact #2

The speed of experiments doubled, driving crucial momentum for Enveda’s official launch in December 2020.

Impact #3

Benchling's solution is designed to scale, which means Enveda won’t have to worry about outgrowing their data foundation.

Benefit #1	66% of scientists said they had a more cohesive view of experimental progress
Benefit #2	130% increase in data integrity
Benefit #3	230% reported average increase in speed to discovery

Benefit #1

66% of scientists said they had a more cohesive view of experimental progress

Benefit #2

130% increase in data integrity

Benefit #3

230% reported average increase in speed to discovery

Download PDF Version

Overview

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

Operational Impact

Quantitative Benefit