metadata
license: other
license_name: bigbind
tags:
- molecular-docking
- gnina
- drug-discovery
- structure-based-virtual-screening
pretty_name: BigBind GNINA-Docked Poses
size_categories:
- 1M<n<10M
configs:
- config_name: default
data_files:
- split: train
path: bigbind_gnina_scores.parquet
BigBind GNINA-Docked Poses
GNINA redocking of every protein-ligand pair in BigBind (train/val/test, including SNA decoy variants). Each ligand is docked into its BigBind pocket and rescored with GNINA's CNN scoring function.
Files
| File | Contents |
|---|---|
results.tar.gz |
All docked poses, one folder per protein target: results/{protein_id}/{ligand}.sdf |
bigbind-docked-vs/{protein_id}.tar.gz |
Same poses, re-split per target so a single protein can be fetched without downloading the whole archive |
bigbind_gnina_scores.parquet |
One row per ligand, with pose-derived score columns (below) |
Scores schema
| Column | Description |
|---|---|
protein_id |
BigBind pocket identifier |
ligand_smiles |
Ligand SMILES |
label |
1 = active, 0 = inactive/decoy (from BigBind) |
n_poses |
Number of poses GNINA returned |
gnina_best_* |
Score at the pose GNINA's CNN rates most likely correct (minimized_affinity, cnn_score, cnn_affinity, cnn_vs, cnn_affinity_var) |
gnina_mean_*, gnina_std_* |
Mean / population std of each score across all poses |
gnina_docked_status |
Whether GNINA returned any pose for this ligand |
docked_pose_sdf_path |
Pose path relative to results/, e.g. 1433S_HUMAN_1_233_0/mol_4699.sdf |
compressed_docked_pose_sdf_path |
Where to find that same pose inside bigbind-docked-vs/, e.g. bigbind-docked-vs/1433S_HUMAN_1_233_0.tar.gz/results/1433S_HUMAN_1_233_0/mol_4699.sdf |
Column layout follows the Kingldore/dude-vs schema.
Docking parameters
Docked with GNINA v1.3.2 (master:f23dd2b), run with --cnn_scoring rescore --exhaustiveness 8 --num_modes 9 --seed 42.
License
Derived from BigBind; usage is subject to BigBind's original license terms.
Citation
Brocidiacono, M., Francoeur, P., Aggarwal, R., Popov, K. I., & Koes, D. R. (2024). BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.3c01211