Revvity and Eli Lilly have announced a collaboration to expand access to AI drug discovery models for biotechnology researchers. The partnership integrates Lilly’s predictive modelling tools into the Revvity Signals platform to support data-driven discovery workflows.
AI drug discovery models integrated into Signals platform
Revvity confirmed that Lilly’s TuneLab predictive modelling tools will be available through the Signals Xynthetica platform. The system allows research teams to apply advanced machine learning models within existing laboratory data environments.
The models have been trained using decades of research data generated by Eli Lilly and are designed to support early-stage drug discovery and experimental design.
How AI drug discovery models support modern research
Artificial intelligence is increasingly used to identify drug candidates, predict molecular interactions and optimise experimental workflows. Machine learning systems can analyse large datasets generated during discovery programmes to identify patterns that may accelerate therapeutic development.
AI drug discovery models are particularly valuable when integrated directly with laboratory data platforms that capture and manage experimental results.
Federated learning approach protects proprietary data
The collaboration uses a federated learning framework that allows organisations to contribute experimental data to improve predictive models without sharing sensitive datasets directly.
Participating research groups can apply Lilly’s models to their own discovery programmes while maintaining control of proprietary data.
Joint funding to support biotech participation
As part of the collaboration, Revvity and Lilly will co-fund access for selected biotechnology organisations. Participants will receive access to Signals One and Signals Xynthetica software, along with modelling resources.
The approach is intended to lower barriers to AI adoption in drug discovery while enabling organisations of different sizes to contribute to shared predictive model development.
Further developments in AI-driven drug discovery can be found in the Digital Health & Health Tech section of Life Sciences Global News.
