README.rst

DockTGrid

Generate voxel representations of protein-ligand complexes for deep learning applications.

📌 Features

• GPU-accelerated voxelization of protein-ligand complexes.
• Easy customization of voxel grid channels and parameters.
• Readily usable with PyTorch.
• Support for multiple file formats (to be expanded).
  • ✅ PDB
  • ✅ MOL2

🚀 Getting Started

Installation (pip)

Install DockTGrid using pip:

$ python -m pip install docktgrid

Development

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Python 3.11 is recommended, other versions may work but are not tested.

Clone the repository:

$ git clone https://github.com/gmmsb-lncc/docktgrid.git
$ cd docktgrid

Create a new environment using venv and activate it:

$ python3.11 -m venv env
$ source env/bin/activate

Or if you prefer using conda:

$ conda create --prefix ./venv python=3.11
$ conda activate ./venv

Install the required packages:

$ python -m pip install -r requirements.txt

Run the tests:

$ python -m pytest tests/

🖥️ Usage

See the documentation for more information on how to use DockTGrid.

There are also some examples in the notebooks folder.

📄 License

This project is licensed under the LGPL v3.0 license.

📝 Citation

If you use DockTGrid in your research, please cite:

• da Silva, M. M. P., Guedes, I. A., Custódio, F. L., & Dardenne, L. E. (2024). DockTGrid (0.0.2). Zenodo. https://zenodo.org/doi/10.5281/zenodo.10304711

@software{mpds2024docktgrid,
    author       = {da Silva, Matheus Müller Pereira and
              Guedes, Isabella Alvim and
              Custódio, Fábio Lima and
              Dardenne, Laurent Emmanuel},
    title        = {DockTGrid},
    month        = mar,
    year         = 2024,
    publisher    = {Zenodo},
    version      = {0.0.2},
    doi          = {10.5281/zenodo.10304711},
    url          = {https://zenodo.org/doi/10.5281/zenodo.10304711}
    }