domd-toolkit 1.0.0

A modular platform for constructing molecular dynamics simulations (Chemical Compiler).

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DoMD

DoMD is a toolkit for atomistic molecular dynamics modelling.

Installation

Quick installation for the release

# Download and unzip the release zip file
$ cd <path-to-domd>
$ conda env create -f environment.yml
$ conda activate domd-toolkit
$ python -c 'from domd_tools import *; print("Success install domd.")'

Step-by-step installation from repo

We recommend using conda to manage your environment. Follow the steps below to set up DoMD:

1. Create and Activate the Conda Environment

conda create -n domd-toolkit -c conda-forge python==3.12 nomkl numpy rdkit=2025.03.6 openbabel numba networkx pandas scipy jupyter scikit-learn matplotlib MDAnalysis
conda activate domd-toolkit

2. Install PyTorch and Additional Dependencies

pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cpu
pip3 install torch_geometric pdbreader

3. Download the Toolkit

You can obtain the DoMD toolkit via GitHub or by downloading our official release. Please choose one of the following options:

Option A: Clone from GitHub (Requires manual database download)

1. Clone the repository:

   git clone https://github.com/DoMD-toolkit/DoMD.git
   

2. Important: Download the required forcefield database opls.db (large file) from Google Drive Link.
3. Move opls.db into the following directory: DoMD/domd_forcefield/oplsaa/resources/opls.db

Option B: Download the Release DoMD.zip (Recommended) Download the latest DoMD.zip file from the Releases page. The opls.db file is already included in the compressed package, so no extra downloads are necessary. Unzip the file before proceeding.

4. Install DoMD

Navigate to the root directory of the project (where setup.py is located) and install it in editable mode:

cd DoMD
pip install -e .

Usage Examples & Testing

Navigate to the polyimide example directory:

cd <path-to-the-examples>/pi

1. End-to-End Workflow

Run the main script to process a pre-equilibrated Coarse-Grained (CG) configuration:

python polyimides.py

• Outputs: * chemfast.gro: The back-mapped All-Atom (AA) conformation.
  • chemfast.top: The GROMACS-compatible force field and topology file.
  • out_chemfast.xml: The PyGAMD xml intput
• Purpose: These files are ready for immediate use in atomistic simulations using GROMACS.

---

2. Step-by-Step Module Testing

We also provide individual tests for specific S-CGFG functions to demonstrate the underlying workflow:

• CG Topology Generation

  python cg.py
  

  Generates an initial CG configuration (e.g., linear chains) and force field parameters based on HSP (Hansen Solubility Parameters) predictions. This is typically used for pre-equilibration or reaction runs. (Note: This step is optional as a pre-equilibrated configuration is already provided).

  • Output: out_chemfast_cg.xml file for PyGAMD, and cg_params.txt as CG forcefield parameters.

• CG Parameterization

  python cg_params.py
  

  Generates CG simulation force field parameters only from specific monomers and reaction templates.

  • Output: cg_parameters.txt

• Back-mapping (CG to FG)

  python fg.py
  

  Tests the Coarse-Grained to Fine-Grained (AA) conversion.

  • Outputs: AA conformations (stored in the aa_confs/ folder) and topology metadata (meta_aa_top.pkl).

• Force Field Parameterization

  python ff.py
  

  Performs force field parameterization by reading meta_aa_top.pkl.

  • Output: meta_ffs.pkl

• Final Assembly

  python output.py
  

  Assembles the AA conformations and force field data into standard GROMACS input formats.

  • Outputs: Final .gro, .top and .xml (for PyGAMD) files.

Large Files & Databases

Due to file size limits, our large database files are hosted externally. You can download them from this Google Drive Link.

• domd_forcefield/oplsaa/resources/opls.db (Required) This is the core database necessary for standard force field assignment and running the toolkit.
• domd_database/forcefield/oplsaa/data/ligpargen/AllData.pkl (Optional) This file is strictly used for training the ML force field models. You can ignore this file if you are only running standard simulations.

Documentation

• Online: Access the latest manuals, API references, and tutorials at our official Documentation Site.
• Offline: You can also browse the documentation locally by opening docs/build/html/index.html from your cloned repository in any web browser.