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Increase ML model accuracy for your drug discovery programs

Gain access to more diverse and complementary data to generate new biological insights and deliver better drugs.
Elevate internal capability and skills with the benefits of complementary data and continually evolving model development.

Data-driven AI at the forefront of research

Proprietary data sits in silos and cannot be shared. Public data is not sufficiently diverse. Acquiring research-grade data is time-consuming and costly.

Access to biological data and multi-modal patient data is emerging as the key to differentiation. To unlock the potential of AI in drug discovery along its value chain, pharma and biotech companies need to train and validate ML models on more diverse data.

Computational access to more diverse data

Asset policies ensure only permitted computations run on data, privacy and IP is protected, with only anonymous results or model weights being shared. Improve model accuracy and generalizability with access to complementary industry data.

Data doesn't move

Leverage your own data in combination with third parties, without centralizing or directly sharing data. Data owners stay in control of their data, always. Computations are sent to federated data.

Achieve model generalizability, faster

Leverage your own data in combination with third-party data and achieve better model accuracy and generalizability.

From 0 to trusted collaboration

A secure, connecting layer between all parties to combine the strength of pharma companies, academic institutes, and technology providers.

Trustworthy AI for accelerated drug discovery

Computational governance, together with federated learning, allows PharmaCo's to securely collaborate across suppliers and partners. Gain compliant access to sensitive biomedical data and increase the robustness and quality of your ML models.

Improve model accuracy and generalizability

Leverage your own data in combination with third-party data for a new level of insights in early drug discovery.

By utilizing your own data in combination with third-party data, achieve better model accuracy and generalizability.

Designed with privacy and security at the core

Keep your data and model IP protected

Computational governance

Clearly define permitted computations and statistical functions for each dataset and user. Automatically validate incoming computational requests against configured asset policies.

Model approval and security

Rapidly test if models from private or academic model providers work well on your data. Model cards provide details on a model’s performance, limitations, and the privacy controls in place. Review models before they are run. Inspect and evaluate model code changes. Approve or reject in the same workflow.

Audit and oversight

Trace any activity on data, including who accesses what data, when, and for what purpose. Monitor health and behavior of models. Flag anomalies and act before issues escalate.

Straightforward onboarding of new partners

Quickly enable people in new partner organizations to take part in collaborations by creating the right asset policies. Define who can run what algorithms on data with the appropriate privacy controls.

Access data across boundaries

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

Integrate AI into research workflows

Working together to unlock the hidden value of sensitive data for AI

Multi-modal AI powered biomarkers

Enhancing drug efficacy and safety profiles

Collaborative research across institutions

Training more accurate models

It is likely, however, that a considerably larger number of protein-small molecule crystal structures have been solved in industry but are not publicly available–if it were possible to access such structures to train more accurate and robust versions of RFAA (and other similar networks) considerable public good in the form of improved medicines could result.

Baker Lab Institute for Protein Design, University of Washington
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Learn more

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.

In this white paper, you will learn:

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

In this case study you will learn:

checkmark How two pharma companies use protein-ligand interaction data and AI models to predict target binding of novel compounds
checkmark How a pharma company fueld their drug discovery activities by leveraging molecule data from their suppliers
checkmark How an AI-focussed health tech company developed AI models to improve decision-making processes for cancer treatments
White Paper

Privacy and Security Whitepaper

Privacy and security considerations are an important part of adopting a federated infrastructure for machine learning and analytics. This whitepaper outlines the various security techniques and privacy controls used by Apheris to safely collaborate and build data applications and AI across organizational and geographical boundaries.

In this whitepaper you will learn about:

checkmark The privacy and security landscape, and how Apheris operates within it
checkmark Defining attack vectors and threat models as the basis for a privacy and security strategy
checkmark Using the 5 Safes as a framework for privacy and security in federated ML and analytics
checkmark Practical advice for making the move to a federated data infrastructure
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.

In this white paper, you will learn:

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

Ready to talk?

Find out how you can use Apheris for faster operationalization of trustworthy AI Drug Discovery programs