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Dataset for: Predicting Experimental Success in De Novo Binder Design: A Meta-Analysis of 3,766 Experimentally Characterised Binders
Benchmarking dataset for the publication: Predicting Experimental Success in De Novo Binder Design: A Meta-Analysis of 3,766 Experimentally Characterised BindersInlcludes the following data: final_data.csv: Dataset with all collected binder features with th
File Size8.7 GB
Data TypeDataset
Published2025
Licensecc-by-4.0
Total Views2,638
Total Downloads4,900
Benchmarking dataset for the publication: Predicting Experimental Success in De Novo Binder Design: A Meta-Analysis of 3,766 Experimentally Characterised Binders
Inlcludes the following data:
- final_data.csv: Dataset with all collected binder features with the following relevant columns
- binder_id: name of binder
- target_id: name of target
- binder: binary experimental binding info
- source: source publication of the binder
- All other columns are the features described in the publication
- input_pdbs.tar.zst: Input .pdb files used for analysis where file names correspond to binder_id in the final_data.csv; sourced from public available de novo binder design campains
- AF2_initial_guess_outputs.tar.zstd: Output .pdb files generated with the initial guess AF2 implementation (https://github.com/nrbennet/dl_binder_design)
- AF3_outputs.tar.zstd: Compressed output from AF3 including structure (.cif) and confidence files (.json) with one subfolder per binder_id
- Boltz1_outputs.tar.zstd: Compressed output from Boltz-1 including structure (.cif), summary confidence (.json) and pae/plddt (.npz) files with one subfolder per binder_id
- ColabFold_outputs.tar.zstd: Compressed output from ColabFold including structure (.pdb) and confidedence files (.json)
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Files are hosted on the source repository. Click download to access the full dataset.
Overath, Max Daniel (2025). Dataset for: Predicting Experimental Success in De Novo Binder Design: A Meta-Analysis of 3,766 Experimentally Characterised Binders. https://doi.org/10.5281/zenodo.15722219