Eli Lilly has launched Lilly TuneLab, a new artificial intelligence and machine learning (AI/ML) platform designed to provide biotech companies with access to powerful drug discovery models. These models have been trained on decades of Lilly’s own proprietary research data, which the company estimates to have cost over $1 billion to obtain. The platform is intended to act as an "equalizer," giving smaller companies access to the same AI capabilities that Lilly's own scientists use, with the ultimate goal of accelerating the creation of new medicines for patients.

Lilly TuneLab is powered by Lilly's comprehensive datasets on drug disposition, safety, and preclinical results, representing experimental data on hundreds of thousands of unique molecules. The platform operates on a federated learning model, a privacy-preserving approach that allows partner biotechs to use the AI models without either party having to directly share their proprietary data. In return for access, selected biotech partners contribute their own training data, which helps to continuously improve the models for everyone's benefit. The company plans to expand the platform's capabilities in the future, including adding in vivo small molecule predictive models that will be exclusively available on Lilly TuneLab.

This new initiative is the latest addition to Lilly's Catalyze360 program, which offers a suite of resources to its biotech partners. This program includes strategic capital through Lilly Ventures, access to advanced lab facilities at Lilly Gateway Labs, and drug development expertise from Lilly ExploR&D. According to Dr. Nisha Nanda, head of Lilly Catalyze360, the platform is designed to help early-stage biotech companies overcome a fundamental hurdle: the lack of access to large-scale, high-quality data needed to train effective AI models. By sharing these resources, Lilly aims to compress decades of learning into instantly accessible intelligence, enabling its partners to unlock novel scientific insights, make smarter development decisions earlier, and increase their likelihood of success.