deepdrivewe-academy

Implementation of DeepDriveWE using Academy.

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

Installation

The project supports both uv and pip. A pinned uv.lock is committed for reproducible installs.

With uv (recommended)

git clone git@github.com:ramanathanlab/deepdrivewe-academy.git
cd deepdrivewe-academy
uv sync

uv sync creates a .venv/, installs the project in editable mode, and resolves all dependencies from uv.lock. Activate the environment with source .venv/bin/activate, or prefix commands with uv run (e.g. uv run python).

With pip

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

Full installation with MD dependencies

OpenMM and AmberTools are best installed via conda. After creating the conda env, you can use either uv or pip for the Python package:

git clone git@github.com:ramanathanlab/deepdrivewe-academy.git
cd deepdrivewe-academy
conda create -n deepdrivewe python=3.10 -y
conda activate deepdrivewe
conda install omnia::ambertools -y
conda install conda-forge::openmm==7.7 -y
pip install -e .   # or: uv 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, install the project with the dev and docs extras and set up the pre-commit hooks.

With uv (recommended — uses uv.lock for reproducible installs):

uv sync --extra dev --extra docs
uv run pre-commit install

With pip:

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:

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

# pip / activated venv
pre-commit run --all-files
tox -e py310

When a dependency in pyproject.toml changes, refresh the lock file with uv lock and commit uv.lock alongside the change.

Building the Documentation

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

# uv
uv sync --extra docs
uv run properdocs serve

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

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

properdocs build --strict   # or: uv run 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}
}