Transform your scientific data into AI-ready insights
Learn how leading biopharmas are unlocking the full potential of their scientific data to achieve key scientific outcomes.
The Scientific AI Gap
For many biopharma companies, there is a gap between the vision for AI and the present reality. They struggle to launch AI initiatives or capitalize on previous efforts. Why? The answer centers on data. In this white paper, you’ll learn the 3 primary obstacles to Scientific AI and how to close the AI gap.
Scientific AI Companion for Data Analysis
TetraScience's new AI-powered tool transforms how scientists analyze their data. With simple conversational prompts, they can retrieve data and generate visualizations—in just seconds. Tetra Scientific AI will help scientists save time, eliminate errors, and gain deeper insights into their data.
This demo shows how a scientist can quickly aggregate and analyze data from over 500 samples in a gene editing experiment, without manual data handling or processing.
AI Assistant for Lead Clone Selection
Lead clone selection is usually a time-consuming and resource-intensive phase in cell line development. However, AI is poised to transform this process.
Introducing the Tetra Lead Clone Selection Assistant, powered by Tetra Scientific Data and AI Cloud™ and Google Cloud Vertex AI. This application can predict the most promising lead clones in silico, enabling faster and better decision making. As a result, it has the potential to shorten lead clone selection from five months to just one month.
Key takeaways: Scalable data strategies for scientific AI-enabled breakthroughs
AI stands on the brink of revolutionizing the biopharmaceutical industry. It promises to slash time to market, lower costs, mitigate risks, and create novel therapies. However, the main barrier to realizing these benefits is not the development of AI models, but rather the quality and accessibility of the scientific data that feeds them. Learn the essentials from an expert discussion on AI featuring AWS and TetraScience to jumpstart your AI journey.
Unlock full potential of scientific data
Findable, Accessible, Interoperable, and Reusable (FAIR) data helps life science organizations innovate. This process requires collecting, transforming, and standardizing scientific data to make it searchable, ready for analytics, and able to flow freely among instruments and applications. Learn how harmonized scientific data in the cloud can help biopharma companies make the most of their data and accelerate groundbreaking scientific outcomes.