Case Studies Harnessing AI to create next-generation medicines

Edit This Case Study Record

	Harnessing AI to create next-generation medicines

Harnessing AI to create next-generation medicines

Technology Category	Analytics & Modeling - Machine Learning Application Infrastructure & Middleware - Data Exchange & Integration
Applicable Industries	Healthcare & Hospitals Life Sciences
Applicable Functions	Product Research & Development Quality Assurance
Use Cases	Predictive Quality Analytics
Services	Data Science Services
Challenge	Absci, a drug and target discovery company, was facing several challenges in its operations. The success of their work heavily relied on coordination across teams. However, they were managing sample handoffs in spreadsheets, which not only had the risk of error but also lacked sophisticated collaboration features, making it difficult to share and reference data. Additionally, the performance of their AI models depended on the quality of the training data. They occasionally experienced data-related deviations such as duplicate or incomplete datasets and risked copy/paste errors. Lastly, they lacked a universally accessible tool for data connectivity, making it difficult for stakeholders to drive organizational and scientific decisions. Read More
About Customer	Absci is a drug and target discovery company that harnesses deep learning AI and synthetic biology to expand the therapeutic potential of proteins. Through its Integrated Drug Creation™ Platform, Absci is able to identify novel drug targets, discover optimal biotherapeutic candidates, and generate the cell lines to manufacture them in a single efficient process. Biotech and pharma innovators partner with Absci to create the next generation of protein-based drugs. The company is based in Vancouver, Washington, US, and has between 51 to 250 employees. They operate in the Biotechnology Research industry. Read More
Solution	Absci adopted Benchling to address their challenges. Benchling provided a formal request handoff data model that tracks sample handoffs and makes it possible to manage data in a way that is machine readable. This resulted in substantially improved operational efficiency in the laboratory. Benchling’s unique data model flexibility, API, and data warehouse capabilities allowed Absci to develop critical applications in the Benchling platform that enabled seamless integration with its distributed data model and application ecosystem. Absci uses Benchling’s custom registry schema to connect disparate data types, manage metadata associations, store experiment outcomes, and link to large quantities of raw data that they use to train AI models. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Application Infrastructure & Middleware - Data Exchange & Integration

Applicable Industries

Healthcare & Hospitals

Life Sciences

Applicable Functions

Product Research & Development

Quality Assurance

Use Cases

Predictive Quality Analytics

Services

Data Science Services

Challenge

Absci, a drug and target discovery company, was facing several challenges in its operations. The success of their work heavily relied on coordination across teams. However, they were managing sample handoffs in spreadsheets, which not only had the risk of error but also lacked sophisticated collaboration features, making it difficult to share and reference data. Additionally, the performance of their AI models depended on the quality of the training data. They occasionally experienced data-related deviations such as duplicate or incomplete datasets and risked copy/paste errors. Lastly, they lacked a universally accessible tool for data connectivity, making it difficult for stakeholders to drive organizational and scientific decisions.

About Customer

Absci is a drug and target discovery company that harnesses deep learning AI and synthetic biology to expand the therapeutic potential of proteins. Through its Integrated Drug Creation™ Platform, Absci is able to identify novel drug targets, discover optimal biotherapeutic candidates, and generate the cell lines to manufacture them in a single efficient process. Biotech and pharma innovators partner with Absci to create the next generation of protein-based drugs. The company is based in Vancouver, Washington, US, and has between 51 to 250 employees. They operate in the Biotechnology Research industry.

Solution

Absci adopted Benchling to address their challenges. Benchling provided a formal request handoff data model that tracks sample handoffs and makes it possible to manage data in a way that is machine readable. This resulted in substantially improved operational efficiency in the laboratory. Benchling’s unique data model flexibility, API, and data warehouse capabilities allowed Absci to develop critical applications in the Benchling platform that enabled seamless integration with its distributed data model and application ecosystem. Absci uses Benchling’s custom registry schema to connect disparate data types, manage metadata associations, store experiment outcomes, and link to large quantities of raw data that they use to train AI models.

Impact #1	Improved quality of data capture
Impact #2	Increased throughput across their screening funnel
Impact #3	Improved data accessibility and interoperability between teams

Impact #1

Improved quality of data capture

Impact #2

Increased throughput across their screening funnel

Impact #3

Improved data accessibility and interoperability between teams

Download PDF Version

Overview

Harnessing AI to create next-generation medicines

Operational Impact