deepdrivewe-academy

Implementation of DeepDriveWE using Academy.

📖 Documentation: https://ramanathanlab.github.io/deepdrivewe-academy

Installation

To install the package, run the following command:

git clone git@github.com:ramanathanlab/deepdrivewe-academy.git
cd deepdrivewe-academy
pip install -e .

Full installation including dependencies:

git clone git@github.com:ramanathanlab/deepdrivewe-academy.git
cd deepdrivewe-academy
conda create -n deepdrivewe python=3.10 -y
conda install omnia::ambertools -y
conda install conda-forge::openmm==7.7 -y
pip install -e .

To use deep learning models, install the correct version of PyTorch for your system and drivers. To use mdlearn, you may need an earlier version of PyTorch:

pip install torch==1.12

Contributing

For development, it is recommended to use a virtual environment. The following commands will create a virtual environment, install the package in editable mode, and install the pre-commit hooks.

python -m venv venv
source venv/bin/activate
pip install -U pip setuptools wheel
pip install -e '.[dev,docs]'
pre-commit install

To test the code, run the following command:

pre-commit run --all-files
tox -e py310

Building the Documentation

Documentation is built with ProperDocs (a continuation of MkDocs 1.x).

pip install -e '.[dev,docs]'
properdocs serve

Then open http://localhost:8000 in your browser. For a production build:

properdocs build --strict

Citation

If you use DeepDriveWE in your research, please cite:

Leung, J. M. G.; Frazee, N. C.; Brace, A.; Bogetti, A. T.; Ramanathan, A.; Chong, L. T. "Unsupervised Learning of Progress Coordinates during Weighted Ensemble Simulations: Application to NTL9 Protein Folding." Journal of Chemical Theory and Computation 2025, 21 (7), 3691--3699. DOI: 10.1021/acs.jctc.4c01136

BibTeX:

@article{leung2025unsupervised,
  title={Unsupervised Learning of Progress Coordinates during Weighted Ensemble Simulations: Application to NTL9 Protein Folding},
  author={Leung, Jeremy MG and Frazee, Nicolas C and Brace, Alexander and Bogetti, Anthony T and Ramanathan, Arvind and Chong, Lillian T},
  journal={Journal of chemical theory and computation},
  volume={21},
  number={7},
  pages={3691--3699},
  year={2025},
  publisher={ACS Publications}
}