OpenBind Structure–Affinity Data Release: Enterovirus A71 (EV-A71) / Coxsackievirus A16 (CVA16) 2A protease

This record contains the first public dataset released by the OpenBind consortium: a structure–affinity dataset for structure-based AI and computational drug discovery.

The dataset focuses on the EV-A71 2A protease and includes 925 crystallographic binding events from 699 compounds, with associated affinity measurements for 601 compounds. The release links experimentally determined protein–ligand structures with binding affinities, providing a resource for model training, fine-tuning, benchmarking, error analysis, and method development.

This target was selected in coordination with the AI-driven Structure-enabled Antiviral Platform (ASAP) Discovery Consortium, a global antiviral discovery center for pandemic preparedness focused on delivering therapeutics for globally equitable and affordable access.

The data were generated from a crystallographic fragment screen and follow-on compounds. Affinity measurements are reported as KD values measured using the Creoptix WAVEsystem. Experimental work was carried out using Coxsackievirus A16 (CVA16) 2A protease as a surrogate system for EV-A71 2A protease. These two proteins differ at only five positions in the amino acid sequence, none of which are near the active site.

Unlike many public protein–ligand resources, this dataset provides both structural and affinity information across a dense, single-target experimental campaign. This makes it useful for asking whether models can recover observed binding modes, capture changes across related compounds, predict affinity trends, and identify where current structure-based AI methods fail.

Related resources

• Benchmark repository: https://github.com/OpenBind-Consortium/EV-A71_2A_benchmark

• OpenBind: https://openbind.uk/

• Fragalysis: Download / Viewer

• Experimental protocols: OpenBind protocols.io workspace

License

The dataset is released under the CC0 1.0 Universal license.

Acknowledgements

OpenBind received funding from the UK Department for Science, Innovation and Technology under grant number G2-SCH-2025-06-16537.