Federated learning and analytics for drug discovery

Empower researchers to build pipeline and bring new drugs to market quicker, with machine learning and analytics on distributed, cross-organization data.

Read the Whitepaper

Drug discovery is hungry for more data.

Biotech companies are driving major advances in data-driven drug discovery by leveraging data sharing and collaboration.
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But concerns around regulatory compliance with HIPAA and GDPR and concerns with protecting valuable IP limit life science organizations' willingness to share patient data at scale, and restrict innovation.

The MLOps toolkit for decentralized patient data.

With integrate.ai's production-ready, API-based platform, stand up federated machine learning and analytics across data on your platform in less than a week. Access more patient data for drug discovery to accelerate pipeline development.

Learn more about integrate.ai

Use data for machine learning and analytics without moving the data.

Protect personal health information with differential privacy.

Integrate seamlessly and quickly using our SDK.

Save engineering time with managed infrastructure.

Deploy easily on integrate.ai secure architecture.

Empower users with tools for the entire data science workflow.

Accelerate your drug discovery product roadmap.

Enable access to more data on your platform, including omics, imaging, histological, and clinical data, so that researchers can build better models and accelerate drug discovery.

Open data platforms: Expand collaboration with access to more patient health data on your platform, without having to worry about pooling all the data.

AI-augmented drug discovery platforms: Accelerate target selection and compound screening with high performance models trained on more data from more partners.

Federated learning and analytics for drug discovery

Drug discovery is hungry for more data.

The MLOps toolkit for decentralized patient data.

Accelerate your drug discovery product roadmap.

Learn

Frédéric Ratle

The Data Conundrum: Open Source AI Is Not As Open As You Think

Frédéric Ratle

The Data Conundrum: Open Source AI Is Not As Open As You Think

Steve Irvine

integrate.ai Selected as Federated Learning Technology Partner to Power One of World’s Largest Precision Health Consortia

Steve Irvine

integrate.ai Selected as Federated Learning Technology Partner to Power One of World’s Largest Precision Health Consortia

Nasron Cheong

Identifying the right use cases for federated learning and analytics

Nasron Cheong

Identifying the right use cases for federated learning and analytics

Elliot Gordon

5 cost management best practices for ETL pipelines

Elliot Gordon

5 cost management best practices for ETL pipelines

Nasron Cheong

Design a federated learning system in seven steps

Nasron Cheong

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Federated learning and analytics for drug discovery

PATIENT DATA

Drug discovery is hungry for more data.

integrate.ai

The MLOps toolkit for decentralized patient data.

Use Cases

Accelerate your drug discovery product roadmap.

blog

Learn

Frédéric Ratle

The Data Conundrum: Open Source AI Is Not As Open As You Think

Frédéric Ratle

The Data Conundrum: Open Source AI Is Not As Open As You Think

Steve Irvine

integrate.ai Selected as Federated Learning Technology Partner to Power One of World’s Largest Precision Health Consortia

Steve Irvine

integrate.ai Selected as Federated Learning Technology Partner to Power One of World’s Largest Precision Health Consortia

Nasron Cheong

Identifying the right use cases for federated learning and analytics

Nasron Cheong

Identifying the right use cases for federated learning and analytics

Elliot Gordon

5 cost management best practices for ETL pipelines

Elliot Gordon

5 cost management best practices for ETL pipelines

Nasron Cheong

Design a federated learning system in seven steps

Nasron Cheong

Design a federated learning system in seven steps