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https://github.com/bytedance/hmr

Learning Harmonic Molecular Representations on Riemannian Manifold, ICLR, 2023

https://github.com/bytedance/hmr

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Learning Harmonic Molecular Representations on Riemannian Manifold, ICLR, 2023

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  • Host: GitHub
  • Owner: bytedance
  • License: other
  • Language: Python
  • Default Branch: main
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  • Size: 2.73 MB
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Created over 3 years ago · Last pushed over 3 years ago
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README.md

HMR: Harmonic Molecular Representation on Riemannian Manifold

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

TOC

Dependencies

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

```bash

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

PYMESHPATH="~/PyMesh" # substitute with your own PyMesh path git clone https://github.com/PyMesh/PyMesh.git $PYMESHPATH 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 $PYMESHPATH/thirdparty python build.py all # build third party dependencies cd $PYMESHPATH mkdir build cd build cmake .. make -j # check for missing third-party dependencies if failed to make cd $PYMESHPATH 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

MSMSPATH="~/MSMS" # substitute with your own MSMS path wget https://ccsb.scripps.edu/msms/download/933/ -O msmsi8664Linux22.6.1.tar.gz mkdir -p $MSMSPATH # mark this directory as your $MSMSbin for later use tar zxvf msmsi8664Linux22.6.1.tar.gz -C $MSMSPATH

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

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

Owner

  • Name: Bytedance Inc.
  • Login: bytedance
  • Kind: organization
  • Location: Singapore

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Dependencies

requirements.txt pypi
  • biopython ==1.80
  • easydict ==1.10
  • horovod ==0.27.0
  • jupyter ==1.0.0
  • networkx ==2.6.3
  • nose ==1.3.7
  • numpy ==1.21.5
  • pandas ==1.3.5
  • pdb2pqr ==3.5.2
  • pyquaternion ==0.9.9
  • scikit-learn ==1.0.2
  • scipy ==1.7.3
  • setuptools ==59.5.0
  • tensorboard ==2.11.2
  • torchmetrics ==0.11.1
  • tqdm ==4.64.1
setup.py pypi