Kingldore/lit-pcba-vs

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:

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.

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:

@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:

@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.