Apheris for Pharma

Breakthroughs in healthcare are faster and more reliable with federated data ecosystems. By processing patient data without risking its integrity, data collaboration is safer and more effective than data sharing.
This e-book highlights real-world examples and explains how to implement a federated data ecosystem in pharma and healthcare:

Benefits of federated data ecosystems in pharma and healthcare
Examples in drug discovery, clinical trials and commercial growth
How data collaboration compares with data sharing
How to get started with federated data ecosystems

In recent years, Deep Learning has gained considerable traction in many fields, but none more so than in the realm of drug discovery. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.

In this white paper, you will learn:

How you can preserve intellectual property while generating value from sensitive data
Deep dive into federated and secure QSAR models
How Apheris prevents uncontrolled data sharing
How you can pave the way to precision medicine with federated and secure QSAR model training

In this article, we explore Privacy Enhancing Technologies (PETs) and highlight their need in the context of ML/AI projects that use sensitive or regulated data. We start by demystifying PETs and providing concrete examples to help CTOs and product owners make informed decisions about how to improve data protection.