The Apheris Resource Library

Apheris announced today the expansion of the Federated OpenFold3 Initiative from the AI Structural Biology (AISB) Network.

Co‑folding models, like AlphaFold 3, Boltz‑2, and OpenFold3, can predict the structure of protein–ligand and protein–protein complexes. While these models perform strongly on public benchmarks, their accuracy often decreases on novel targets that are underrepresented in the training data.

Apheris 3.8 improves compute spec management, reduces dependencies for streamlined training, and expands diagnostics, supporting production-scale federated AI in clinical research, multimodal data networks, and computational biology.

v3.7 is here, bringing expanded Python and NVIDIA FLARE support, a more streamlined model management experience, and improvements across the board for Data Scientists and Data Custodians.

OpenFold3, a structure prediction system developed by AlQuraishi Lab at Columbia University, will be fine-tuned using proprietary data from AbbVie and Johnson & Johnson in a confidentiality-preserving and secure federated environment powered by Apheris.

The Apheris Trust Center serves as a comprehensive resource for organizations seeking to uphold the highest standards of security and data privacy. It offers guidance and our certifications and attestations, including ISO 27001 and SOC 2, that customers value as essential components to fulfil their compliance obligations.

Apheris Compute Gateway 3.0 provides an end-to-end federated analytics and machine learning solution that enables everyone to collaborate securely on highly sensitive data.

The new version, 3.5, is packed with new goodies and improvements to make your federated life easier and protecting your data assets even more convenient. Read more

Apheris has closed a Series A funding round, raising total funding to $20.8M. Led by OTB Ventures and eCAPITAL, this round will help Apheris to expand its secure life sciences data networks.

A federated network is defined as a network that spans across geographical or organizational boundaries. Such a federated network contains interconnected nodes that are operationally independent, yet centrally managed for efficiency and ease of use. A federated data network is

With today's release, we continue our journey to provide the simplest, most secure solution for building and joining data networks in life science and drug discovery.

Pharma and biotech companies often lack enough data within their own organizations, limiting AI model accuracy. Read more about my discussions with pharma leaders at this years Drug Discovery Innovation Forum DDIF on overcoming the data access dilemma.

Secure data collaboration brings new capabilities to the pharama and biotech community. Read about applications, use cases and challenges in collaborative settings and how Apheris can help.

Great news, everyone! We released Apheris 3.3. With prior releases, we set the foundation for two really useful improvements for our users today — derived Datasets and Multi-Gateway Organizations.

We are excited to announce that Apheris has officially joined the Global Alliance for Genomics & Health (GA4GH) as an organizational member.

Our journey to secure and standardize GitHub repository configurations at Apheris using Infrastructure-as-code.

Federated Learning and Data Mesh enable decentralized model training with domain-specific governance. This integration improves data privacy, scalability, and regulatory compliance, addressing limitations of traditional systems in regulated sectors where sensitive data is involved.

Discover why data collaboration is the future. Explore how federated learning keeps data safe while promoting collaboration without sharing.

Apheris Compute Gateway 3.2 provides a simpler way to create federated computations, adds federated nnUnet and cox regression and streamlines the data science experience.

Apheris Compute Gateway 3.0 provides an end-to-end federated analytics and machine learning solution that enables everyone to collaborate securely on highly sensitive data.

Open-source frameworks for federated learning are a great way of getting first hands-on experience. Here are our Top 7 with their respective pro and cons

Discover the power of Privacy Enhancing Technologies (PETs) and why they're crucial for your enteprise ML/AI projects involving sensitive or regulated data. We'll demystify PETs with clear examples, equipping you with the knowledge to make informed decisions on how to best protect your data and enhance your products.

In December, the annual NeurIPS conference took place. With the biggest conference in machine learning research happening, I was curious to see what was going on in the world of federated learning. Here are my 5 take-away on how federated learning can be applied in industry today.

Computational governance enables machine learning on European healthcare data.
European hospitals face a unique challenge, balancing complex regulatory frameworks with the need to make valuable data available for research. Computational governance exists as a solution and enables compliant research on health data.

MedTech AI revolution depends on computational access to fine-grained patient data to train enhanced ML models. Apheris provides governed, secure, and private computational access to data for ML, enabling fast and easy FDA approval for new MedTech data products.

Exploring the EU AI Act, a pivotal regulation addressing AI's multifaceted nature, this blog outlines risk levels, high-risk system requirements, and preparation for compliance. Apheris's solution for various governance, privacy, and security obligations is highlighted, emphasizing a shared commitment to responsible AI.

Reaching universal agreement on how to regulate AI is impossible. But a carefully designed cyclical process can lead to efficiencies in an enterprise's journey towards trustworthy, accountable, and sustainable AI. In this blog, I navigate the regulation space through the eyes of a data custodian.

There is so much useful data around us. Yet most data is not accessible for machine learning or other large scale analytics. In this article, we will explore why this is and what needs to be in place to enlighten this dark data.

Machine learning and AI needs domain-specific data to be trained for its various use cases. Often this data is sensitive and falls under various privacy regulations. In this article we will introduce the Apheris Compute Gateway as a solution for contributing sensitive data to ML projects.

Companies are benefiting from federated learning by being able to access data without privacy and IP risks associated with copying or centralizing data.

The clock is ticking. The time to act on climate change is now. In this blog, I'll take a closer look at practical thoughts on climate thinking and action in organizations.

Together with the German Federal Office for Information Security we've developed frameworks and recommendations for ML practitioners to help secure ML models and maintain appropriate security measures.

11 February is the International Day of Women and Girls in Science. We interview Evelyn, Data Science Experience Lead at Apheris, and give you an insight into the life of a successful woman in science.

In our daily work as a company that builds a platform for federated and privacy-preserving data science, we are often asked to clarify concepts around federated learning with customers. This article highlights 7 common myths about federated learning (FL) and, using practical examples, shows you exactly why they are misleading.

How can you evaluate platforms around emerging technologies like federated learning? This article gives you guidance on what your selection criteria should be.

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.

There is only value in data if it can be used, and if there is appropriate access to that data when it is needed. Learn how you can turn decentralized, federated data into a complementary strategic data asset.

Collaborative data ecosystems in pharma securely connect life sciences and healthcare data to drive AI-powered insights or advanced analytics on these distributed datasets. They enable shared use of real-world data without exposing sensitive information, overcoming data silos while ensuring privacy and compliance.