HMR: Harmonic Molecular Representation on Riemannian Manifold

This is the code repository for our ICLR 2023 paper Learning Harmonic Molecular Representations on Riemannian Manifold

Dependencies

This work was developed and tested under pytorch 1.10.0 with CUDA 11.3. Please install dependencies as follows:

# We recommend using conda for environment management
conda create -n HMR python=3.7.3 
conda activate HMR

pip install -r requirements.txt

# install PyMesh for surface mesh processing
PYMESH_PATH="~/PyMesh" # substitute with your own PyMesh path
git clone https://github.com/PyMesh/PyMesh.git $PYMESH_PATH 
cd $PYMESH_PATH 
git submodule update --init
apt-get update
# make sure you have these libraries installed before building PyMesh
apt-get install cmake libgmp-dev libmpfr-dev libgmpxx4ldbl libboost-dev libboost-thread-dev libopenmpi-dev
cd $PYMESH_PATH/third_party
python build.py all # build third party dependencies
cd $PYMESH_PATH
mkdir build
cd build
cmake ..
make -j # check for missing third-party dependencies if failed to make
cd $PYMESH_PATH
python setup.py install
python -c "import pymesh; pymesh.test()"

# install meshplot
conda install -c conda-forge meshplot

# install libigl
conda install -c conda-forge igl

# download MSMS
MSMS_PATH="~/MSMS" # substitute with your own MSMS path
wget https://ccsb.scripps.edu/msms/download/933/ -O msms_i86_64Linux2_2.6.1.tar.gz
mkdir -p $MSMS_PATH # mark this directory as your $MSMS_bin for later use
tar zxvf msms_i86_64Linux2_2.6.1.tar.gz -C $MSMS_PATH

# install PyTorch 1.10.0 (e.g., with CUDA 11.3)
conda install pytorch==1.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.10.0+cu113.html

# install HMR
pip install -e . 

Reproduce paper results

Please refer to each folder under tasks for details on reproducing results from the paper. Data and models can be downloaded from Zonodo (https://zenodo.org/record/7686423#.ZAq_9ezMJf1).

Citation

@inproceedings{
wang2023learning,
title={Learning Harmonic Molecular Representations on Riemannian Manifold},
author={Yiqun Wang and Yuning Shen and Shi Chen and Lihao Wang and Fei YE and Hao Zhou},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=ySCL-NG_I3}
}

License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.