Tamarind Bio Selected by Eli Lilly as Technology Partner for TuneLab2.0

Tamarind Bio, building AI infrastructure for biotech R&D, led by Deniz Kavi, Sherry Liu, and the team, has been selected by Lilly TuneLab as a third-party technology partner to build, host, and operate the inference infrastructure layer for TuneLab2.0, which is now live. 

Lilly Launches Collaborative AI Drug Discovery Platform.

Eli Lilly and Company has introduced Lilly TuneLab, a collaborative AI and machine learning platform designed to accelerate drug discovery across the biotech industry.

The platform gives participating companies access to AI models trained on decades of Lilly's proprietary research data, including studies involving hundreds of thousands of unique molecules. Companies that join the network can also contribute their own data through privacy-preserving federated learning, allowing the models to improve collectively without requiring participants to share sensitive underlying datasets.

By combining Lilly's extensive research history with data contributed by partners, TuneLab aims to create more accurate and generalizable models for discovering and developing new medicines.

Building The Infrastructure For AI-Driven Drug Discovery.

Since its founding, Tamarind Bio has developed infrastructure that gives scientists access to more than 300 molecular design and prediction tools, supporting tens of thousands of researchers across the pharmaceutical and biotechnology industries.

The company has focused on the foundational challenges of making advanced AI models practical for scientific use, including reliable API delivery, secure multi-tenant access, and user-friendly interfaces designed around researchers' workflows. By prioritizing usability and accessibility, Tamarind aims to help scientists integrate sophisticated computational tools into everyday drug discovery and development processes.

Powering The Next Generation Of TuneLab.

As a technology partner for TuneLab 2.0, Tamarind Bio has helped design user-friendly scientific workflows and provides the scalable infrastructure needed to run biomolecular AI models for each participating company within its own secure, private environment.

Meanwhile, Rhino Federated Computing will continue to operate the federated learning infrastructure that enables member companies to collaboratively improve AI models while keeping their proprietary data private and under their control.