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 now shift their focus from the inward to the outward, collaborate on sensitive data and share domain expertise.

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Our Customers & Partners

BCG logo
Technische Universität Darmstadt logo
Gaia X  logo
Apha  logo
COREVAS logo
Federal Ministry of Education and Research

“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 and inaccessible, incomplete, or non-standardized. 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.

Solution

Breakthrough innovation in health ecosystems requires more than just sharing data. Apheris allows deep and trusted collaboration during all stages of the data science workflow and beyond. Starting from identifying the most valuable use cases, to standardizing and leveraging FAIR data, establishing interoperability between tech stacks, the training of robust machine learning models to the full governability and reproducibility of data and AI models between all collaborating organizations.

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Federated Learning

Deep access to granular data and insights, without having to move and centralize data. Federated Learning results in increased machine learning performance, while respecting data ownership and privacy.

data centric

Data-centric AI

Machine learning models are only as good as the data they are being trained on. Apheris enables a data-centric view on ML innovation and supports the entire data science workflow – from data acquisition to model operationalization.

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Research-grade Data

Apheris helps to curate and standardize available and future data sets to ensure interoperability and enables access to longitudinal data that is complete, standardized, and prospective.

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Privacy-enhancing Technologies

Besides a federated architecture, Apheris leverages different PETs with the highest level of precision to establish governance, privacy, and security, as well as 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

Federated Learning on Vertically Distributed Healthcare Data

Joining complementary data sets creates stronger machine learning capabilities. But valuable data containing intellectual property cannot be shared and must adhere to regulatory frameworks. Healthcare companies can use federated learning to work with decentralized data, allowing secure collaboration without sharing data.

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.

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.