The Apheris federated learning and analytics platform for drug discovery
Register data in the Compute Gateway
- Protein-ligand interaction data
- Omics data
- Molecular data (SMILES format)
- RWE data
- Medical images
Using the SDK
- OpenFold
- Nextflow
- RDKit
- Survival analytics (Python)
AI in drug discovery
3D structure prediction
Build federated data networks
Accelerate drug discovery pipeline
Why choose Apheris for federated ML and analytics
IP protection of data
Integrated SDK
Scalability
Compliance
"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."
Learn more
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.
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: 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.