DockM8_v1.1_Lit-PCBA_benchmarking_data_part1

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DockM8 — Lit-PCBA benchmark data, part 1 of 3 (corrected, v1.1.1)================================================================ This record contains benchmarking data for the Lit-PCBA datasetproduced with DockM8 v1.1.1. It is intended for benchmarkingscoring functions and machine-learning models, and as a largearchive of pre-docked poses generated by multiple dockingalgorithms. This is the CORRECTED release: RTMScore and GenScore were recomputedagainst a corrected whole-residue binding pocket, and the per-posescoring tables, the per-scoring-function selected poses, and theconsensus/performance results were regenerated to match. All otherscoring functions and the docking poses themselves are unchangedfrom the original run. Lit-PCBA is split across THREE downloads because of its size. Thisis part 1 of 3. Part 1 (this record): adrb2 aldh1 esr1a esr1t fen1 Part 2: idh1 kat2a mapk1 Part 3: gba mtorc1 pkm2 pparg tp53 vdr Coverage (all parts): 14 targets, docked with 3 programs(gnina, plants, smina). Note: the target oprk1 is not included. Single download: lit-pcba_1.tar.bz2 Archive layout (nested compression)----------------------------------- The files are NOT directly browsable — the download is a compressedarchive of compressed archives of gzipped SDFs. Use the`extract_zenodo.py` helper from the DockM8 repository to unpack them. lit-pcba_1.tar.bz2 # outer archive (this download)└── phase2/ └── [target].tar.bz2 # one inner archive per target └── [target]/ ├── [target]_activity_data.csv # experimental active/decoy labels ├── [target]_docking_library.sdf.gz # compounds docked ├── [target]_ligand.sdf.gz # reference ligand(s) ├── [target]_pocket.pdb / .pdbqt # docking box (unchanged) ├── [target]_protein.pdb # original structure ├── [target]_protein_prepared.pdb # prepared for docking ├── [target]_corrected_10A_pocket.pdb # corrected whole-residue pocket [v1.1.1] ├── [target]_corrected_10A_pocket.pdb.json # pocket fingerprint [v1.1.1] └── results/ ├── [target]_prepared_library.sdf.gz ├── corrected_inputs/ # controlled activity universe per program [v1.1.1] │ └── [target]_[program]_{activity_data,universe_activity,excluded}.csv ├── [program]/ # per docking engine (gnina, plants, smina) │ ├── [program]_poses.sdf.gz # raw docked poses │ ├── [program]_poses_rescored.sdf.gz # poses + scores (RTM/Gen corrected) │ ├── [program]_poses_rescored.csv # same, as a table │ └── [program]_poses_corrected_rtm_genscore.csv # corrected RTM/Gen per pose [v1.1.1] ├── scores/ │ └── [target]_[program]_[SF]_scores.csv # best-pose score per SF ├── performance/ │ └── [target]_[program]_[SF]_performance.csv # consensus enrichment per SF └── clustering/ └── [target]_[program]_[SF]_selected_poses.sdf.gz # selected pose per compound per SF [SF] runs over each scoring function and its consensus combinations.The four corrected scoring functions are RTMScore, GenScore-scoring,GenScore-docking and GenScore-balanced. How to extract-------------- Clone the DockM8 repo (main branch) and run the helper script — itreverses all three compression layers in one command: git clone https://github.com/DrugBud-Suite/DockM8.git cd DockM8 # Put the downloaded archive(s) in a folder, then: python -m analysis.extract_zenodo /path/to/downloads /path/to/output \ --archives lit-pcba_1.tar.bz2 To reassemble the full Lit-PCBA benchmark, download all three partsinto the same folder and extract them together: python -m analysis.extract_zenodo /path/to/downloads /path/to/output \ --archives lit-pcba_1.tar.bz2,lit-pcba_2.tar.bz2,lit-pcba_3.tar.bz2 The script will: 1. Extract the outer .tar.bz2, 2. Extract each per-target inner .tar.bz2, 3. Decompress every *.sdf.gz in place. Output layout: /path/to/output/lit-pcba/PART_1/ IMPORTANT: Do NOT attempt manual extraction with `tar -xjf` alone —you will be left with nested tarballs and thousands of unreadable.sdf.gz files. Always use the helper script.

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