UCL joins global €60m AI-driven drug discovery initiative to generate open datasets and accelerate new medicines using AI models across disease areas
A new global AI-driven drug discovery project has launched with a €60 million budget, with University College London (UCL) among the lead academic partners. The LIGAND‑AI consortium will develop large, open datasets and train advanced AI models to predict how molecules bind to proteins, speeding up early drug discovery for conditions including rare neurological and cancer diseases, and making the data freely available to researchers worldwide.
A global consortium for open data and collaboration
The LIGAND-AI consortium brings together 18 partners across nine countries. It will generate large, high-quality, open datasets of how molecules (ligands) bind to proteins. These datasets will be used to train AI models to predict candidate molecules as suitable binders for thousands of human proteins for use in medications.
The consortium is led by Pfizer and the Structural Genomics Consortium (SGC), a consortium of experts across academia, the life sciences industry, technology companies, and research organisations, which will investigate thousands of proteins relevant to existing and unmet disease areas, including rare, neurological, and oncological (cancer) conditions.
The project aims to generate open, accessible, high-quality, AI-ready data at scale as a public resource.
Transforming drug discovery with AI and collaboration
Drug discovery is expensive and uncertain. Scientists spend years testing molecules to find just one that binds to the disease-related protein.
LIGAND-AI aims to change this by combining advanced laboratory technologies with computational methods to create a seamless pipeline from experiment to prediction. The consortium will generate billions of data points using complementary screening technologies, enabling researchers worldwide to develop, train, and benchmark AI models that predict molecular interactions.
Additionally, LIGAND-AI will create an open discovery ecosystem by inviting the scientific community to work together and redefine drug discovery for diseases. The consortium will bring together experts from protein science, structural biology, chemistry, and machine learning.
Expert perspectives on open science and AI
Professor Matthew Todd said, “Machine learning will accelerate the discovery of new medicines. But for that to happen, we need very large, high-quality public datasets to effectively train the algorithms. This project helps to generate that dataset of how billions of molecules bind human proteins – experimentally, in the lab. This will help everyone develop better predictive models for drug discovery, including UCL’s many industry neighbours in King’s Cross.
“As a global, open science project, we’re encouraging potential partners to be in touch, particularly in the areas of protein science and machine learning.”
Professor Aled Edwards (University of Toronto), CEO of the Structural Genomics Consortium and project coordinator, said: “This project brings together scientists and companies from across disciplines within an open science ecosystem. It is heartening to see these diverse scientific communities coalesce around a common vision to generate and share valuable chemical data openly with the world.”
