| --- |
| license: other |
| pretty_name: LIT-PCBA Docked Poses (gnina virtual screening) |
| tags: |
| - chemistry |
| - molecular-docking |
| - virtual-screening |
| - drug-discovery |
| - gnina |
| - lit-pcba |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: litpcba_gnina_scores.parquet |
| --- |
| |
| # LIT-PCBA Docked Poses (gnina virtual screening) |
|
|
| The docked poses from `lit-pcba_vs.tgz`, produced by the Koes lab at the |
| University of Pittsburgh for *Virtual Screening with Gnina 1.0*. |
|
|
| The original is one large archive that must be read start to finish. Here it |
| is split by target into `lit-pcba-vs/`, one archive per LIT-PCBA target, so |
| you can download only the targets you need: |
|
|
| ```bash |
| hf download Kingldore/lit-pcba-vs lit-pcba-vs/FEN1.tar.gz --repo-type dataset |
| tar -xzf lit-pcba-vs/FEN1.tar.gz |
| ``` |
|
|
| 15 targets, 833 GB in total. The contents are unchanged from the original; |
| only the packaging differs. |
|
|
| ## Cross-docking |
|
|
| Unlike DUD-E, LIT-PCBA targets have several crystal structures (1 to 15 per |
| target), and every screening compound was docked into *every* structure for |
| its target, not just one. So a compound has one score per receptor structure |
| it was docked into, not one score total — the row key below is |
| `(target, receptor_pdb, compound_id)`. |
|
|
| Each archive also contains a small number of redock files, where a |
| structure's own co-crystallized ligand is re-docked into every structure for |
| that target. These are a pose-recovery check, not screening compounds — they |
| carry no active/decoy label and are left out of the score table below. |
|
|
| ## Score table |
|
|
| `litpcba_gnina_scores.parquet` summarises every docked pose into one row per |
| `(target, receptor_pdb, compound_id)` — 16,672,528 rows, 74,950 actives and |
| 16,597,578 inactives. |
|
|
| | column | meaning | |
| |---|---| |
| | `protein_id` | LIT-PCBA target name | |
| | `receptor_pdb` | archive path to the receptor structure this row was docked against | |
| | `compound_id` | PubChem CID | |
| | `label` | 1 = active, 0 = inactive | |
| | `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 | |
| | `docked_pose_sdf_path` | archive path to the SDF containing this compound's poses against this receptor | |
|
|
| The three score families are `minimized_affinity`, `cnn_score`, and |
| `cnn_affinity`. `minimized_affinity` is the empirical docking score, from the |
| column gnina's summary files label `Vina`. |
|
|
| All three `gnina_best_*` values come from the **same** pose — the one with |
| the highest CNNscore — rather than each column being maximised independently. |
|
|
| `gnina_std_*` is null for compounds with a single pose. One row (IDH1) has a |
| null `gnina_best_cnn_score`/`gnina_best_cnn_affinity`: gnina's own scoring |
| output was missing those two values for that pose. |
|
|
| **Original download:** <https://bits.csb.pitt.edu/files/gninavs/lit-pcba_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} |
| } |
| ``` |
|
|
| **LIT-PCBA** — the benchmark set that was docked: |
|
|
| ```bibtex |
| @article{tran2020lit, |
| title = {LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual |
| Screening}, |
| author = {Tran-Nguyen, Viet-Khoa and Jacquemard, C{\'e}line and Rognan, Didier}, |
| journal = {Journal of Chemical Information and Modeling}, |
| volume = {60}, |
| number = {9}, |
| pages = {4263--4273}, |
| year = {2020}, |
| doi = {10.1021/acs.jcim.0c00155} |
| } |
| ``` |
|
|
| ## Links |
|
|
| - Paper: <https://doi.org/10.3390/molecules26237369> |
| - LIT-PCBA paper: <https://doi.org/10.1021/acs.jcim.0c00155> |
| - gnina software: <https://github.com/gnina/gnina> |
| - LIT-PCBA: <https://drugdesign.unistra.fr/LIT-PCBA/> |
| - 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. |
|
|
