DockM8_v1.1_Lit-PCBA_benchmarking_data_part2

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DockM8 v1.1 — Lit-PCBA benchmark data (Part 2 of 3)==================================================== This repository contains benchmarking data for the Lit-PCBA datasetproduced with DockM8 v1.1. It is intended for benchmarking scoringfunctions and machine-learning models, and as a large archive ofpre-docked poses generated by multiple docking algorithms. Because of Zenodo's per-record size limit, the Lit-PCBA data is splitacross THREE companion records, which must all be downloaded: lit-pcba_1.tar.bz2 lit-pcba_2.tar.bz2 lit-pcba_3.tar.bz2 Archive layout (nested compression)----------------------------------- The files are NOT directly browsable — each Zenodo download is acompressed archive of compressed archives of gzipped SDFs. You mustuse the `extract_zenodo.py` helper from the DockM8 v1.1 repositoryto unpack them. lit-pcba_N.tar.bz2 # outer archive (Zenodo download)└── phase2/ └── [target_name].tar.bz2 # one inner archive per Lit-PCBA target └── [target_name]/ ├── [target]_activity_data.csv # experimental binding data ├── [target]_docking_library.sdf # compounds to dock ├── [target]_ligand.sdf # reference ligand(s) ├── [target]_pocket.pdb # binding-site definition ├── [target]_pocket.pdbqt # binding site (AutoDock format) ├── [target]_protein.pdb # original structure ├── [target]_protein_prepared.pdb # prepared for docking └── results/ ├── allposes.sdf.gz # combined poses (gzipped) ├── allposes_rescored.csv # scores for all poses ├── [target]_prepared_library.sdf.gz ├── [docking_program]/ # per-engine outputs │ ├── [program]_poses.sdf.gz # raw poses (gzipped) │ └── [program]_poses_rescored.sdf.gz ├── performance/ │ └── [target]_[program]_[pose_selection]_performance.csv ├── scores/ │ └── [target]_[program]_[pose_selection]_scores.csv └── clustering/ └── [target]_[program]_[pose_selection]_selected_poses.sdf.gz 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 all three downloaded archives in the same folder, then: python -m analysis.extract_zenodo /path/to/downloads /path/to/output # Or extract only specific parts: python -m analysis.extract_zenodo /path/to/downloads /path/to/output \ --archives lit-pcba_1.tar.bz2,lit-pcba_2.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/ ├── PART_2/ └── PART_3/ 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. Companion records----------------- DEKOIS 2.0 benchmark data: [Zenodo DOI] DUD-E benchmark data: [Zenodo DOI] DockM8 source code: https://github.com/DrugBud-Suite/DockM8

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