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Protein-ligand data generation at scale to support computational hit finding and optimization

Project description

Advancing and democratising small-molecule drug discovery

The discovery and optimisation of small-molecule ligands using physics-based methods, machine learning (ML), and AI could revolutionise the development of new drugs and research tools, particularly for rare and paediatric diseases, cancer, and women’s health. However, progress is limited by the lack of high-quality protein-ligand data in the public domain. The EU-funded LIGAND-AI project aims to identify potential drug candidates for over 500 human proteins, revolutionising and democratising small-molecule drug discovery. A diverse public-private partnership will produce high-quality, machine-learning-ready open protein-ligand data at scale, serving as a public resource. Additionally, it will engage with the research community to develop, benchmark, and evaluate new machine-learning models for small-molecule discovery and optimisation.

Objective

The ability to discover and optimize small-molecule ligands by using innovative physics-based, machine learning (ML) and artificial intelligence (AI) approaches would transform and democratize the discovery of new small molecule drugs and related research tools (chemical probes), with important implications for the development of new medicines for rare and pediatric diseases, cancer, and women’s health. The development of these methods is being held back by the lack of high-quality protein-ligand data in the public domain. To fill this gap, we have assembled a multi-sector public-private partnership that will generate high-quality, ML-ready, open protein-ligand data at scale and as a public good, and will engage with the research community to build, benchmark and test new machine learning models for small molecule discovery and optimization. Our ambition is to identify drug starting points for >500 human proteins and transform and democratize small molecule drug discovery.

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-JU-RIA - HORIZON JU Research and Innovation Actions

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Call for proposal

(opens in new window) HORIZON-JU-IHI-2025-09-single-stage

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Coordinator

STRUCTURAL GENOMICS CONSORTIUM LBG
Net EU contribution
€ 2 677 741,25
Address
EUSTON ROAD 215
NW1 2BE London
United Kingdom
Region
London Inner London — West Camden and City of London
Activity type
Research Organisations
Links
Total cost
€ 4 677 741,25

Participants (16)

Partners (5)