prodock 0.2.0

ProDock — automation utilities for molecular docking workflows.

ProDock

Automatic pipeline for molecular modeling

Overview

ProDock is a toolkit for building automated molecular docking workflows. It is designed for campaigns involving multiple receptors, multiple ligands, and multiple docking engines, with support for downstream pose extraction, interaction profiling, visualization, and SQLite-backed result management. Please visit Documentation for more details.

The project aims to provide one consistent workflow for:

• receptor and ligand preparation
• batch docking across one or more engines
• pose extraction into standardized tables
• interaction analysis from docked complexes
• database storage for poses, scores, and interactions
• reproducible downstream analysis

This makes ProDock useful both for small docking experiments and for larger benchmark-style or screening-style studies.

Main capabilities

Docking workflow

ProDock supports automated docking workflows across multiple receptor-ligand combinations and can be organized as single-target, multi-ligand, or multi-receptor campaigns.

Typical use cases include:

• one receptor with many ligands
• many receptors with one ligand set
• many receptors with many ligands
• comparison of multiple docking engines on the same campaign

Post-processing

After docking, ProDock can parse docking outputs and convert them into structured pose tables with canonical columns such as:

• receptor_id
• ligand_id
• engine
• pose_rank
• affinity
• mol
• pose_id

These standardized tables make it easier to compare poses across engines and campaigns.

Interaction analysis

ProDock supports protein-ligand interaction extraction and summarization, enabling residue-level interaction profiles for each pose. This is intended for downstream comparison, ranking, and interpretation of docking results.

Database-backed storage

ProDock includes SQLite-based storage to keep docking poses and interaction records in a structured and queryable form. This is especially useful when handling many receptors, ligands, docking engines, and poses.

Database architecture

ProDock stores docking outputs and associated interaction information in a relational SQLite database. This supports scalable querying, reproducible analysis, and easy export into pandas dataframes.

Database architecture figure:

This architecture is intended to support:

• pose-centric storage
• stable pose identifiers
• interaction lookup by pose, receptor, ligand, or engine
• multi-receptor and multi-engine benchmarking workflows
• clean integration with pandas- and RDKit-based analysis

Step-by-Step Installation Guide

1. Python Installation: Ensure that Python 3.11 or later is installed on your system. You can download it from python.org.

2. Creating a Virtual Environment (Optional but Recommended): It's recommended to use a virtual environment to avoid conflicts with other projects or system-wide packages. Use the following commands to create and activate a virtual environment:

python -m venv prodock-env
source prodock-env/bin/activate  

Or Conda

conda create --name prodock-env python=3.11
conda activate prodock-env

3. Cloning and Installing SynTemp: Clone the SynTemp repository from GitHub and install it:

git clone https://github.com/Medicine-Artificial-Intelligence/ProDock.git
cd ProDock
pip install -r requirements.txt
pip install black flake8 pytest # black for formating, flake8 for checking format, pytest for testing

Reproducing the Case Study

To reproduce the case study, first clone and install ProDock as described above. Then run the command-line workflow using the provided configuration files:

python -m prodock \
  --config Data/case/config.json \
  --receptor-json Data/case/receptor.json \
  --ligand-json Data/case/ligand.json

This command executes the case-study docking workflow using:

• Data/case/config.json: global workflow and docking settings
• Data/case/receptor.json: receptor definitions
• Data/case/ligand.json: ligand definitions

Setting Up Your Development Environment

Before you start, ensure your local development environment is set up correctly. Pull the latest version of the main branch to start with the most recent stable code.

git checkout main
git pull

Working on New Features

1. Create a New Branch:
   For every new feature or bug fix, create a new branch from the main branch. Name your branch meaningfully, related to the feature or fix you are working on.

   git checkout -b feature/your-feature-name
   

2. Develop and Commit Changes:
   Make your changes locally, commit them to your branch. Keep your commits small and focused; each should represent a logical unit of work.

   git commit -m "Describe the change"
   

3. Run Quality Checks:
   Before finalizing your feature, run the following commands to ensure your code meets our formatting standards and passes all tests:

   ./lint.sh # Check code format
   pytest Test # Run tests
   

   Fix any issues or errors highlighted by these checks.

Integrating Changes

1. Rebase onto Staging:
   Once your feature is complete and tests pass, rebase your changes onto the staging branch to prepare for integration.

   git fetch origin
   git rebase origin/staging
   

   Carefully resolve any conflicts that arise during the rebase.

2. Push to Your Feature Branch: After successfully rebasing, push your branch to the remote repository.

   git push origin feature/your-feature-name
   

3. Create a Pull Request: Open a pull request from your feature branch to the staging branch. Ensure the pull request description clearly describes the changes and any additional context necessary for review.

Important Notes

• Direct Commits Prohibited: Do not push changes directly to the main or staging branches. All changes must come through pull requests reviewed by at least one other team member.
• Merge Restrictions: The main branch can only be updated from the staging branch, not directly from feature branches.

Publication

ProDock: From multi-target consensus docking into database-backed storage

License

This project is licensed under MIT License - see the License file for details.

Acknowledgments

This work has received support from the Korea International Cooperation Agency (KOICA) under the project entitled “Education and Research Capacity Building Project at University of Medicine and Pharmacy at Ho Chi Minh City”, conducted from 2024 to 2025 (Project No. 2021-00020-3).