Case Study: Pharma

Learn how to leverage federated and sensitive data at scale and how you can collaborate with your partners to advance and evolve the practice of life sciences.

In this case study you will learn:


How two pharma companies use protein-ligand interaction data and AI models to predict target binding of novel compounds


How a pharma company fueld their drug discovery activities by leveraging molecule data from their suppliers


How an AI-focussed health tech company developed AI models to improve decision-making processes for cancer treatments

Recommended for you

White Paper

Privacy-preserving Data Ecosystems in Support of Drug Discovery

In recent years, Deep Learning has gained considerable traction in many fields, but none more so than in the realm of drug discovery. Although machine learning in its various forms has been deployed for drug discovery and development for several decades, the era of Big Data has created a niche for Deep Learning. Applications range from generating de novo hit-like molecules, predicting drug-disease associations and activity, toxicology estimation, to the analysis of medical images.

White Paper

Privacy-preserving Data Science on QSAR models

This case study demonstrates the utility and effectiveness of Apheris’ platform and services for data science on not directly accessible and distributed data. An integral part of our platform is the implementation of federated learning, which decentralizes the learning algorithms to access a number of different data sets while maintaining privacy. This process eliminates the need to pool sensitive data, which has been a major hurdle in practice due to a lack of trust between different data providers.


The Three Adoption Stages of Privacy-enhancing Technologies (And Why We Are Stuck on Level Two)

PETs are massively changing how we operate, and how we have to think about the data and AI landscape. Introducing such a game-changer into large enterprises has to be done with the highest precision, and a lot of foresight.