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Foundational AI models for drug discovery have made a huge leap forward over the last 2 years. However, limited availability of high-quality molecular data is a major bottleneck to achieving the next steps forward. The AI Structural Biology Consortium was created to tackle these challenge and unblock the next revolutions in AI-driven drug discovery.

In this case study we explore:

The challenges around access to high-quality molecular data for Life Sciences
How the Consortium consisting of major pharmaceutical companies, prestigious model building partners and Apheris as the technology provider was created to tackle this
The use cases including model benchmarking & fine-tuning
Leveraging Federation to ensure secure, governed and privacy preserving collaboration between all parties

Explore an overview of data privacy, industry, and forthcoming AI regulations, examining their intersections in data usage. Delve into the emerging opportunities and challenges, amidst existing risks, and discover how to navigate this intricate regulatory terrain effectively with Apheris.

Every organization using AI or ML in Europe needs to pay attention - the clock is ticking!
Are you prepared for the upcoming EU AI Act? Read our infographic to learn more!

Discover the significance of agility and compliance in today's evolving regulatory landscape and delve into how a federated learning approach, backed by robust governance, can empower organizations to innovatively and securely harness sensitive data for trusted AI solutions.

The EU AI Act marks a crucial step in regulating AI, addressing inherent risks and fostering innovation. Applicable to entities within and outside the EU, this article delves into the EU AI Act's structure, the urgency of preparation, and offers insights into achieving compliance.

A leading data aggregator in healthcare research partnered with Apheris to provide life sciences customers with granular access to patient data while ensuring GDPR compliance. Apheris enabled fully federated machine learning, optimizing the model training process and enhancing customer satisfaction by addressing long-standing GDPR issues. This partnership illustrates Apheris's potential to unlock data assets securely and efficiently.

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.

In this white paper, you will learn:

About vertically distributed data
Workflow of privacy-preserving data science
Deep-dive into privacy-preserving record linkage
Deep-dive into federated and privacy-preserving analytics

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:

How federated learning, privacy testing and other tools that enable data science on not directly accessible and distributed data
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.

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:

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

DevOps and MLOps are common methodologies in every company that wants to become software and data science driven by weaving AI into the core fabric of their business. Read what is required to securely collaborate with partners on data and AI at scale.