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AI for drug discovery

Enable your researchers with more accurate and generalizable predictive models. Increase efficiencies across every step of pharmaceutical research and development.

The Apheris federated learning and analytics platform for drug discovery

Biotech and pharma companies need to access large volumes of high-quality data to transform drug discovery with AI at scale. Apheris enables organizations to access data that spans across geographical and organizational boundaries, while protecting the intellectual property rights of the data custodians.

Register data in the Compute Gateway

Data custodians register their biomedical data while keeping the data secure in its original location:
  • Protein-ligand interaction data
  • Omics data
  • Molecular data (SMILES format)
  • RWE data
  • Medical images

Using the SDK

Lightweight and easy to install, integrate, and use:
  • OpenFold
  • Nextflow
  • RDKit
  • Survival analytics (Python)
EXAMPLE USE CASES

AI in drug discovery

3D structure prediction

Re-train protein structure prediction models or other pretrained large language models (LLMs) on sensitive third-party data. Rapidly bring novel breakthrough medicines to patients by leveraging the latest advancement in foundational models such as AlphaFold-2, for example.

Build federated data networks

Start groundbreaking data collaborations with your suppliers and partners. Jointly learn from distributed, sensitive datasets among consortium partners while protecting IP and privacy.

Accelerate drug discovery pipeline

Gain an unbeatable competitive edge for your AI-powered drug discovery pipelines. Enhance your ML models with third-party sensitive data across all data modalities including multi-omics, molecular data, clinical trial data, for example.

Why choose Apheris for federated ML and analytics

IP protection of data

Apheris federated infrastructure ensures that data always stays where it resides and under the full control of the data custodian.

Integrated SDK

Pharma customers can leverage their existing model IP and preserve their model and data pipelines.

Scalability

The Apheris Platform scales to very large computational workloads as required for advanced drug discovery AI pipelines, such as large language models (LLM), for example.

Compliance

Apheris offers state-of-the art security, privacy and governance to ensure compliance across regulatory requirements.

Drug discovery

"We have unique AI models that work best if we fine-tune them to the data of our pharma customers. So far, it was almost impossible to access IP-sensitive data. Apheris has changed this so that we can now re-train our models while keeping the pharma data within their environments – this has been a paradigm shift for us."

CTO, Top-10 drug discovery AI scale-up

Learn more

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.

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.

Case Study

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.

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.