Building a Backbone for Machine Learning Increases Speed of Discovery by 230%
Company Size
11-200
Region
- America
Country
- United States
Product
- Benchling
Tech Stack
- Machine Learning
- Computational Metabolomics Platform
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Innovation Output
- Productivity Improvements
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
About The 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.
The 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.
The 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.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Hospital Inventory Management
The hospital supply chain team is responsible for ensuring that the right medical supplies are readily available to clinicians when and where needed, and to do so in the most efficient manner possible. However, many of the systems and processes in use at the cancer center for supply chain management were not best suited to support these goals. Barcoding technology, a commonly used method for inventory management of medical supplies, is labor intensive, time consuming, does not provide real-time visibility into inventory levels and can be prone to error. Consequently, the lack of accurate and real-time visibility into inventory levels across multiple supply rooms in multiple hospital facilities creates additional inefficiency in the system causing over-ordering, hoarding, and wasted supplies. Other sources of waste and cost were also identified as candidates for improvement. Existing systems and processes did not provide adequate security for high-cost inventory within the hospital, which was another driver of cost. A lack of visibility into expiration dates for supplies resulted in supplies being wasted due to past expiry dates. Storage of supplies was also a key consideration given the location of the cancer center’s facilities in a dense urban setting, where space is always at a premium. In order to address the challenges outlined above, the hospital sought a solution that would provide real-time inventory information with high levels of accuracy, reduce the level of manual effort required and enable data driven decision making to ensure that the right supplies were readily available to clinicians in the right location at the right time.
Case Study
Gas Pipeline Monitoring System for Hospitals
This system integrator focuses on providing centralized gas pipeline monitoring systems for hospitals. The service they provide makes it possible for hospitals to reduce both maintenance and labor costs. Since hospitals may not have an existing network suitable for this type of system, GPRS communication provides an easy and ready-to-use solution for remote, distributed monitoring systems System Requirements - GPRS communication - Seamless connection with SCADA software - Simple, front-end control capability - Expandable I/O channels - Combine AI, DI, and DO channels
Case Study
Driving Digital Transformations for Vitro Diagnostic Medical Devices
Diagnostic devices play a vital role in helping to improve healthcare delivery. In fact, an estimated 60 percent of the world’s medical decisions are made with support from in vitrodiagnostics (IVD) solutions, such as those provided by Roche Diagnostics, an industry leader. As the demand for medical diagnostic services grows rapidly in hospitals and clinics across China, so does the market for IVD solutions. In addition, the typically high cost of these diagnostic devices means that comprehensive post-sales services are needed. Wanteed to improve three portions of thr IVD:1. Remotely monitor and manage IVD devices as fixed assets.2. Optimizing device availability with predictive maintenance.3. Recommending the best IVD solution for a customer’s needs.
Case Study
HaemoCloud Global Blood Management System
1) Deliver a connected digital product system to protect and increase the differentiated value of Haemonetics blood and plasma solutions. 2) Improve patient outcomes by increasing the efficiency of blood supply flows. 3) Navigate and satisfy a complex web of global regulatory compliance requirements. 4) Reduce costly and labor-intensive maintenance procedures.
Case Study
Harnessing real-time data to give a holistic picture of patient health
Every day, vast quantities of data are collected about patients as they pass through health service organizations—from operational data such as treatment history and medications to physiological data captured by medical devices. The insights hidden within this treasure trove of data can be used to support more personalized treatments, more accurate diagnosis and more advanced preparative care. But since the information is generated faster than most organizations can consume it, unlocking the power of this big data can be a struggle. This type of predictive approach not only improves patient care—it also helps to reduce costs, because in the healthcare industry, prevention is almost always more cost-effective than treatment. However, collecting, analyzing and presenting these data-streams in a way that clinicians can easily understand can pose a significant technical challenge.
