| --- |
| license: other |
| tags: |
| - chemistry |
| - molecular-docking |
| - virtual-screening |
| - drug-discovery |
| - gnina |
| - dud-e |
| pretty_name: DUD-E Docked Poses (gnina virtual screening) |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: dude_gnina_scores.parquet |
| --- |
| |
| # DUD-E Docked Poses (gnina virtual screening) |
|
|
| The docked poses from `dude_vs.tgz`, produced by the Koes lab at the University |
| of Pittsburgh for *Virtual Screening with Gnina 1.0*. |
|
|
| The original is one 14.7 GB archive that must be read start to finish. Here it is |
| split by target into `dude-vs/`, one archive per DUD-E target, so you can download |
| only the targets you need: |
|
|
| ```bash |
| hf download Kingldore/dude-vs dude-vs/cdk2.tar.gz --repo-type dataset |
| tar -xzf dude-vs/cdk2.tar.gz |
| ``` |
|
|
| 102 targets, 4,363 files, 14.7 GB in total. The contents are unchanged from the |
| original; only the packaging differs. |
|
|
| ## Score table |
|
|
| `dude_gnina_scores.parquet` summarises every docked pose into one row per |
| `(target, compound_id)` — 1,422,897 rows, 22,784 actives and 1,400,113 decoys. |
|
|
| | column | meaning | |
| |---|---| |
| | `target` | DUD-E target name | |
| | `compound_id` | ZINC or CHEMBL identifier | |
| | `label` | 1 = active, 0 = decoy | |
| | `n_poses` | poses the statistics were computed over | |
| | `gnina_best_*` | value at the pose with the highest CNNscore | |
| | `gnina_mean_*`, `gnina_std_*` | mean and population standard deviation over all poses | |
|
|
| The four score families are `minimized_affinity`, `cnn_score`, `cnn_affinity`, |
| and `cnn_vs` (CNNscore x CNNaffinity, computed per pose then aggregated). |
|
|
| `minimized_affinity` is the empirical docking score, from the column the summary |
| files label `Vina`. DUD-E was docked with default smina arguments |
| (`--seed 0 --autobox_add 4 --num_modes 9`), so this is the Vina scoring |
| function, and `num_modes 9` is why most ligands have nine poses. The pose files |
| are named `*_docked_vinardo.sdf.gz` because Vinardo rescoring was also carried |
| out; those scores are in a separate `vinardo.summary` and are not included here. |
|
|
| All four `gnina_best_*` values come from the **same** pose — the one with the |
| highest CNNscore — rather than each column being maximised independently. |
|
|
| Most ligands have 9 poses. A few are docked more than once against a target; |
| identical pose rows are dropped and the rest pooled, so `n_poses` can exceed 9. |
|
|
| **Original download:** <https://bits.csb.pitt.edu/files/gninavs/dude_vs.tgz> |
|
|
| ## Citation |
|
|
| Please cite both the method and the underlying benchmark. |
|
|
| **gnina virtual screening** — the source of these docked poses: |
|
|
| ```bibtex |
| @article{sunseri2021virtual, |
| title = {Virtual Screening with Gnina 1.0}, |
| author = {Sunseri, Jocelyn and Koes, David Ryan}, |
| journal = {Molecules}, |
| volume = {26}, |
| number = {23}, |
| pages = {7369}, |
| year = {2021}, |
| doi = {10.3390/molecules26237369} |
| } |
| ``` |
|
|
| **DUD-E** — the benchmark set that was docked: |
|
|
| ```bibtex |
| @article{mysinger2012directory, |
| title = {Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and |
| Decoys for Better Benchmarking}, |
| author = {Mysinger, Michael M. and Carchia, Michael and Irwin, John J. and |
| Shoichet, Brian K.}, |
| journal = {Journal of Medicinal Chemistry}, |
| volume = {55}, |
| number = {14}, |
| pages = {6582--6594}, |
| year = {2012}, |
| doi = {10.1021/jm300687e} |
| } |
| ``` |
|
|
| ## Links |
|
|
| - Paper: <https://doi.org/10.3390/molecules26237369> |
| - DUD-E paper: <https://doi.org/10.1021/jm300687e> |
| - gnina software: <https://github.com/gnina/gnina> |
| - DUD-E: <https://dude.docking.org/> |
| - Koes lab file index: <https://bits.csb.pitt.edu/files/> |
|
|
| ## License |
|
|
| This is a mirror hosted for convenience; the data belongs to its original |
| authors and no additional licence is granted here. |
|
|
