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Apheris for Pharma

Collaborate with partners on data and AI to innovate on new medicines, diagnostics and evolve the practice of life sciences
The pharmaceutical industry is undergoing a huge transformation and has made tremendous efforts in recent years to harness the power of data. To realize the full potential of data and AI, research institutes, academia, and large organizations must collaborate on sensitive data and share domain expertise.

Our Customers & Partners

“Apheris' innovative federated data platform allowed us to train robust predictive AI models with two of our partners while keeping all data private, onsite, and safe.”

Industry scientist
at top 20 pharma

Challenges

Current challenges in collaborative research and data sharing in the pharma industry are manifold: valuable data is siloed, inaccessible, or incomplete. Regulatory requirements are ever-changing, and technical complexity makes collaboration unfeasible.

Why Apheris

We enable pioneers to create sustainable data ecosystems that are built on trust, ethics, and deep collaboration. Together with their partners, they can identify new drug candidates, de-risk and accelerate clinical trials and build new diagnostic solutions that improve patient outcomes.
Breakthrough innovation in health ecosystems requires more than just sharing data. Apheris allows deep and trusted collaboration for the training of robust machine learning models. Full governance, privacy, and security for ML ensures data custodians stay in control of data and organizations can collaborate across organizational and geographical boundaries.

Federated learning

Deep access to granular data and insights, without having to move and centralize data. Federated learning results in access to previously inaccessible data, while respecting data ownership.

Data-centric AI

Machine learning models are only as good as the data they are being trained on. Apheris enables governed, private, secure computational access to sensitive data.

Research-grade data

Governed, private, secure computational access to federated data enables pharma companies to access real world data from partner hospitals, labs, or clinics.

Privacy-enhancing technologies

Besides a federated architecture, Apheris leverages multiple PETs to ensure private data stays private and to maintain data quality and usability.

Collaborative data ecosystems in pharma

A collaborative data ecosystem is an alignment of business goals, data and technology, among two or more participants, to collectively create more value than each can create individually. Potential shared value propositions in pharma:
Expand data applications across therapeutic areas
Accelerate drug discovery processes
Enable precision medicine
Build marketplaces based on federated health data

Resources

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. 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:

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

Privacy-preserving Data Science on QSAR models

This case study shows the effectiveness of Apheris’ platform and services for data science on data that is not directly accessible and distributed. Federated learning decentralizes learning algorithms to access multiple data sets, maintaining privacy. Without pooling sensitive data, new insights can be uncovered securely.

In this white paper, you will learn:

checkmark How federated learning, privacy testing and other tools that enable data science on not directly accessible and distributed data
checkmark Sowcase of federated learning, privacy testing and other tools that enable data science practices on not directly accessible and distributed data, at the example of QSAR models.
Guide

E-book - Federated Data Ecosystems in Pharma & Healthcare

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:

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

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