Industry Perspectives

CDD and Lilly partner to bring TuneLab AI models into CDD Vault

The agreement will integrate Lilly’s TuneLab predictive AI models into CDD Vault, giving biotech users access to models trained on decades of proprietary research data.
Written byCollaborative Drug Discovery
| 1 min read
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Collaborative Drug Discovery (CDD), the leading provider of data management solutions for the pharmaceutical and biotechnology industries, today announced an agreement with Eli Lilly and Company (Lilly) to integrate Lilly TuneLab, a collaborative AI/ML drug discovery platform, into CDD Vault.

Lilly TuneLab was created by Lilly to accelerate biotech innovation by enabling participating companies to access models trained on decades of Lilly’s proprietary research data. Through this agreement, biotech companies that use CDD Vault will be able to utilize select Lilly predictive models within their natural scientific workflows.

“By integrating TuneLab directly into CDD Vault, we are advancing CDD’s core vision to enable collaboration across drug discovery teams and organizations. We believe that solving the most complex challenges in drug discovery will depend on innovative collaboration models that provide broad access to research data and empower chemists and biologists to make informed, data-driven decisions,” said Barry Bunin, CDD CEO and President.

“TuneLab’s ADMET models will fit in our trusted secure CDD Vault software environment in natural workflows for experimental and computational scientists and with our growing CDD Vault ecosystem of biopharmaceutical companies," he added.

This agreement paves the way for the planned integration of Lilly TuneLab in both the core and AI modules within CDD Vault for biotech companies that opt in to the program. It builds on CDD’s founding vision from 2004: to demonstrate the economics of efficiency of web-based collaboration.

“TuneLab’s models are synergistic with our innovations such as Zero Click Models, Generative Bioisosteres, as well as Ultrafast Deep Learning similarity to SureChEMBL for novelty and Enamine libraries for convenient SAR-by-catalog,” shared CDD Research Informatics Senior Scientist Peter Gedeck.

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