Silc: Screening InterLocked Chemistry

Dependencies

Silc requires a number of Python packages. In order to install and run silc smoothly, the following requirements and dependencies should be fulfilled:

• python3 (tested for 3.8.18)
• RDKit: https://github.com/rdkit/rdkit
• vina: Python binder of Autodock Vina, see https://autodock-vina.readthedocs.io/en/latest/installation.html#python-bindings
• Meeko: https://github.com/forlilab/Meeko
• py3Dmol (optinoal, but it is used in tutorial): https://pypi.org/project/py3Dmol/
• AmberTool23: https://ambermd.org/GetAmber.php#ambertools
• openmm: http://docs.openmm.org/
• parmed: https://parmed.github.io/ParmEd/html/index.html
• MDAnalysis: https://www.mdanalysis.org/pages/installation_quick_start/

Install this package

Run the following in the package root directory

pip install [-e] .

where the optional -e flag specifies that we want to install in editable mode, which means that when we edit the files in our package we do not need to re-install the package before the changes come into effect. You will need to either restart Python or reload the package though!

Install all dependent packages

on midway2

Run the following:

mamba create -n pysages -c conda-forge python=3.9
mamba activate pysages
mamba install -c conda-forge cuda-version=11.2 cudatoolkit-dev=11.2 cudnn=8.2 cutensor nccl cupy  # CUDA deps
mamba install -c conda-forge cython numba  # compiler tools
mamba install -c conda-forge typing-extensions plum-dispatch  # typing tools
mamba install -c conda-forge importlib-metadata  # python extras

# Install jax
mamba install -c conda-forge ml_dtypes opt-einsum scipy zipp  # jax deps
pip install jaxlib==0.4.2+cuda11.cudnn82 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install jax==0.4.2 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

# Install openmm-dlext, installs openmm as well
mamba install -c conda-forge cuda-version=11.2 openmm-dlext

# Install pysages
pip install git+https://github.com/gustavor101/PySAGES.git@funnel_abf

Tutorial

In the tutorial folder, I demonstrate the usage of this package. You can simply run tutorial_*.ipynb in sequence using Jupyter Notebook.