OpenBind was created to address a bottleneck in structure-based AI: data. We believe that the next generation of structure-based machine learning methods requires better experimental data, not just new architectures. To address this, we are generating dense, high-quality protein–ligand datasets that link structures with binding measurements at a scale rarely available in public resources, supporting model training, fine-tuning, benchmarking, and error analysis.

Today, we are releasing the first public dataset from the OpenBind consortium: a structure–affinity dataset containing 925 crystallographic binding events from 699 compounds, and associated affinity measurements for 601 compounds.