Introducing Lilly TuneLab
A smarter path from molecule to medicine
TuneLab is a collaborative AI platform that supports drug discovery and development. It provides access to powerful AI/ML models trained on decades of Lilly research, expertise and proprietary data; the same models that are used by Lilly's own R&D teams on a daily basis.
Remove data barriers to your preclinical drug development
TuneLab is designed to help drug development teams make better decisions faster.
TuneLab provides:
- A curated set of Lilly proprietary AI/ML foundation models, open source models and models developed in collaboration with, leading industry partners, trained on 2M+ of Lilly’s preclinical datapoints and 675K+ (as of September 2026) of TuneLab partners’ datapoints, including in vivo and in vitro pharmacokinetic and toxicology data
- AI/ML models for both small molecules and antibody-based therapeutics
- A continuously evolving platform, where models improve as new data is contributed by TuneLab members
- A third-party hosted & operated federated learning infrastructure designed to be privacy preserving, to enable AI/ML drug discovery and development collaboration. Each member’s data stays within its own environment
- A web-based, UX-optimized interface that facilitates rapid model interrogation via direct input of SMILES strings and light- and heavy-chain antibody sequences - no AI or ML expertise required
- Seamless integration into existing AI/ML drug discovery workflows with options such as such as API access
- Immediate utility, with access to high performing models from day one
Learn about Lilly TuneLab
Transcript
One year ago Lilly TuneLab™ took off First of its kind.
Collaborative AI-powered drug discovery and development platform Helping biotechs make decisions faster.
50+ AI/ML model endpoints trained on 2M+ data points from decades of Lilly research.
One year on Lilly TuneLab™ is a connected discovery community.
Fast learning, strong innovation.
Every member accelerating bold science.
125+ member companies 500+ users 173 data sets contributed 4 continents represented 9,900+ model runs 600,000+ data points contributed 9M+ inferences made Year one. Just the beginning Now... another journey.
Lilly TuneLab™ Orchestrating the next chapter of discovery.
Lilly TuneLab is designed for biotech companies wanting to use AI to accelerate their preclinical programs.
The advantages of TuneLab apply to organizations in multiple points in the early/preclinical drug-development process, including those:
- Engaged in early/preclinical drug discovery and development
- With a pipeline of at least one small molecule or antibody-based therapeutic
- Looking for data-driven predictions for in vitro ADME and in vivo pharmacology
- Exploring ways to efficiently integrate AI/ML drug development tools into existing workflows
- Interested in leveraging new AI-powered tools, designed to keep your confidential data private and secure
Top FAQs
- What is Lilly TuneLab?TuneLab is a first-of-its-kind, collaborative AI-powered drug-discovery and development platform that gives biotech researchers access to powerful AI models trained on decades of Lilly proprietary research data and member data contributions. These are the same models used by Lilly's own scientists every day.
- Who is Lilly TuneLab designed for?Biotechs with at least one preclinical small molecule or antibody program. Every molecule that advances too far before its liabilities costs time, capital, and can delay the development of new medicines for patients. Lilly TuneLab supports predictive characterization of antibody developability as well as small molecule ADMET profiles prior to in vitro or in vivo validation, with the aim that only the most viable candidates progress, and minimizing dead-ends in drug development.
- How is TuneLab designed to protect proprietary data when training the AI/ML models that use federated learning?TuneLab federated training is hosted by a 3rd party. During a training run, a global AI model is distributed from a central orchestration server to each participating company's node. Each node then trains this model on its proprietary data, with the raw data remaining in that node. Only model updates, such as mathematical weights and parameters, not the underlying molecular structures, are transmitted back to the central server. The server aggregates these updates from all participating nodes to improve the global model, which is then redistributed for the next training round. This iterative process continues until the model converges to optimal performance. To confirm data quality during this process, Tunelab reviews certain aggregated statistics and analytics derived from the data, together with information about assay conditions and protocols used to generate it. This review is designed not to disclose, and does not require access to, any underlying molecular structures, sequences, or individual data points.
- What kinds of models and use-cases does Lilly TuneLab support?TuneLab currently supports preclinical drug-discovery programs, from first synthesis through to in vivo testing, with core models for small molecules and antibodies designed to work across different data sources and in federated environments.
- What training data does a biotech contribute in exchange for access?To become a member of Lilly TuneLab, we are asking small molecule companies to contribute a minimum of 1,000 experimental readouts (an experimental readout is defined as one molecule observed in a wet lab experiment to measure a property). For antibody companies, we are asking a minimum of at least 1 measured property/endpoint across 125 sequences. A number of member companies contribute larger amounts of data than these minimums, with the aim of improving TuneLab’s AI/ML models further, and potentially increasing their performance in that specific member’s projects.
Lilly has spent decades building comprehensive datasets for drug discovery. Today, we're sharing the intelligence gained from that investment to help lift the tide of biotechnology research.
Daniel Skovronsky, M.D., Ph.D.,
Chief Scientific Officer and President, Lilly Research Laboratories
