DockM8 is an all-in-one Structure-Based Virtual Screening workflow based on the concept of consensus docking. The workflow takes care of library and protein preparation, docking, pose selection, rescoring and ranking. We actively encourage the community to participate in the continued development of DockM8. Please see the contribution guide for details.
DockM8 only runs on Linux systems. However, we have tested the installation on Windows Subsystem for Linux v2 and using VirtualBox virtual machines.
For automatic installation, download and run setup_py310.sh This will create the required conda environment and download the respository if not done already. Make sure the installation script can be executed by running chmod +x setup_py310.sh and then **PATH_TO**/setup_py310.sh.
After installation, activate the environment with conda activate dockm8.
Please refer to the Installation Guide provided.
DockM8 comes with a simple form-based GUI for ease of use. To launch it, run the following command :
streamlit run **PATH_TO**/gui.py
You can click the localhost link to access the GUI.
Please refer to the Usage Guide provided.
DockM8 creates organized directory structures based on the workflow mode and library name. The main directory is named dockm8_{library_name} where {library_name} is derived from your input library file.
dockm8_{library_name}/
├── prepared_receptor.pdb
├── pocket_definition.json
├── library/
│ └── prepared_{library_name}.sdf
├── docking/
│ ├── {docking_program}_docked.sdf
│ └── {docking_program}_busted.sdf # Optional, if --bust_poses=True
├── selection/
│ └── {selection_method}_selected.sdf
├── rescoring/
│ ├── rescored_poses.sdf
│ └── rescored_poses.csv
├── consensus/
│ └── {consensus_method}_consensus.csv # Optional, if using >1 scoring function
└── final_results/
├── final_results_with_consensus.sdf # If consensus scoring was used
├── final_results_single_function.sdf # If a single scoring function was used
dockm8_{library_name}/
├── prepared_receptor.pdb
├── pocket_definition.json
│
├── decoy_optimization/ # Optional, if --generate_decoys=True
│ ├── decoys/
│ │ └── {actives_name}_decoys.sdf
│ ├── library/
│ │ └── prepared_{actives_name}_decoys.sdf
│ ├── docking/
│ │ └── {docking_program}/
│ │ ├── {docking_program}_docked.sdf
│ │ └── {docking_program}_busted.sdf
│ ├── pose_selection/
│ │ └── {docking_program}/
│ │ └── {selection_method}/
│ │ └── {selection_method}_selected.sdf
│ ├── rescoring/
│ │ └── {docking_program}/
│ │ └── {selection_method}/
│ │ ├── rescored_poses.sdf
│ │ └── rescored_poses.csv
│ └── performance/
│ ├── activity_data.csv
│ ├── all_performance_results.csv
│ ├── optimal_parameters.json
│ └── {docking_program}_{selection_method}_performance.csv
│
├── library/
│ └── prepared_{library_name}.sdf
├── docking/
│ ├── {docking_program}_docked.sdf
│ └── {docking_program}_busted.sdf # Optional, if --bust_poses=True
├── selection/
│ └── {selection_method}_selected.sdf
├── rescoring/
│ ├── rescored_poses.sdf
│ └── rescored_poses.csv
├── consensus/
│ └── {consensus_method}_consensus.csv # Optional, if using >1 scoring function
└── final_results/
├── final_results_with_consensus.sdf # If consensus scoring was used
├── final_results_single_function.sdf # If a single scoring function was used
dockm8_{library_name}/
├── library/
│ └── prepared_{library_name}.sdf
├── receptor_1/
│ ├── prepared_receptor.pdb
│ ├── pocket_definition.json
│ ├── docking/
│ │ ├── {docking_program}_docked.sdf
│ │ └── {docking_program}_busted.sdf # Optional, if --bust_poses=True
│ ├── selection/
│ │ └── {selection_method}_selected.sdf
│ ├── rescoring/
│ │ ├── rescored_poses.sdf
│ │ └── rescored_poses.csv
│ └── consensus/
│ └── {consensus_method}_consensus.csv # Optional, if using >1 scoring function
├── receptor_2/
│ ├── prepared_receptor.pdb
│ ├── pocket_definition.json
│ ├── docking/
│ │ ├── {docking_program}_docked.sdf
│ │ └── {docking_program}_busted.sdf # Optional, if --bust_poses=True
│ ├── selection/
│ │ └── {selection_method}_selected.sdf
│ ├── rescoring/
│ │ ├── rescored_poses.sdf
│ │ └── rescored_poses.csv
│ └── consensus/
│ └── {consensus_method}_consensus.csv # Optional, if using >1 scoring function
└── final_results/
└── final_ensemble_results.csv
- prepared_receptor.pdb: Processed and prepared protein structure
- pocket_definition.json: Binding site definition and parameters
- library/: Contains prepared compound libraries
- docking/: Docking results from specified programs
- selection/: Selected poses using specified methods
- rescoring/: Rescored poses with various scoring functions
- consensus/: Consensus scoring results (when multiple functions are used)
- final_results/: Final ranked results for virtual screening
- decoy_optimization/: Parameter optimization results (when decoy generation is enabled)
- receptor_N/: Individual receptor results in ensemble mode
-
Open dockm8.ipynb, dockm8_ensemble.ipynb or dockm8_decoys.ipynb in your favorite IDE, depending on which DockM8 mode you want to use.
-
Follow the instructions in the Markdown cells
The analysis/ directory contains everything needed to reproduce all figures from the DockM8 paper: download/extraction scripts, analysis pipeline, and committed literature baselines.
# Quick start (3 commands from project root):
python -m analysis.extract_zenodo /path/to/downloads /path/to/dockm8_data
python -m analysis.run_all all --base-path /path/to/dockm8_dataSee analysis/README.md for complete instructions including download links, archive contents, per-step commands, and output reference.
Please use the issue system built into github to report issues. They will be resolved as soon as possible.
This project is a collaboration between the Volkamer and Hirsch labs.
We acknowledge and thank the authors of the various packages used in DockM8. Please see the publication for citations.
If you use DockM8 in your research, please cite:
TODO: Citation will be added upon publication.
This project is licensed under the GNU GPL v3.0 License - see the LICENSE file for details.
We highly encourage contributions from the community - see the CONTRIBUTING.md file for details.