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Teaming up with a trailblazer to develop next-gen drug discovery
Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries
- Pharmaceuticals
- Healthcare & Hospitals
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Predictive Quality Analytics
- Predictive Maintenance
Services
- Data Science Services
The Challenge
Anagenex, a drug discovery company, is combining DNA encoded library (DEL) technology with machine learning (ML) to accelerate the traditionally time- and labor-intensive portion of drug discovery. However, they faced several challenges. Treating DNA sequencing readouts as an intermediary step to understanding the molecule of interest is an indirect use case, creating an additional layer of complexity. Each experiment can introduce batch effects that mask the biologically relevant effects and confuse the machine learning process. Anagenex needed a robust solution that could start tracking from the very first experiments and scale quickly as the company grows.
About The Customer
Anagenex is a drug discovery company that combines DNA encoded library (DEL) technology with machine learning (ML) to achieve their vision of finding a drug candidate for every disease cheaply and quickly. Their goal is to radically accelerate the traditionally time- and labor-intensive portion of drug discovery with a fast and powerful approach based on combinatorial chemistry, machine learning and rapid, large scale iteration. They’ve relied on Benchling from day one, capturing every intermediate data point to ensure that their computational systems get a complete picture of lab processes. The company is based in the Bay Area, CA and has between 1-10 employees.
The Solution
Anagenex chose Benchling to make sure they would have a digital lab solution that could keep up with its team’s fast pace and cutting edge science. Benchling allowed Anagenex to create a custom data schema in their Benchling Registry that mapped the parent-child relationships between DELs, the DNA tags identifying the building blocks of each DEL, and the pools each DEL was added to. This helped them decode DNA sequencing readouts. Anagenex also used templatized registration in Benchling to lower friction to data entry, keeping scientist compliance up and data records clean and complete. This standardized data entry sped up their ML workflows despite high volumes of data because no extra data cleaning was required.
Operational Impact
Quantitative Benefit
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