AutoMSR

• AutoMSR is an molecular structure automatic representation learning framework for multi-label metabolic pathway prediction.

• The framework of AutoMSR is as follows:

News

• 2021.10.24 Our work AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction is accepted by TCBB 2022.

Installing For Ubuntu 16.04

• Ensure you have installed CUDA 10.2 before installing other packages

1. Nvidia and CUDA 10.2:

[Nvidia Driver] 
https://www.nvidia.cn/Download/index.aspx?lang=cn

[CUDA 10.2 Download and install command] 
#download:
wget https://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
#install:
sudo sh cuda_10.2.89_440.33.01_linux.run

2. Python environment: recommending using Conda package manager to install

conda create -n automsr python=3.7
source activate automsr

3. Pytorch 1.8.1: execute the following command in your conda env automsr

pip install torch==1.8.1+cu102 torchvision==0.9.1+cu102 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html

4. Pytorch Geometric 2.0.2: execute the following command in your conda env automsr

pip install torch-scatter==2.0.9 torch-sparse==0.6.12 torch-cluster==1.5.9 torch-spline-conv==1.2.1 torch-geometric==2.0.2 -f https://data.pyg.org/whl/torch-1.8.0+cu102.html

5. Ray 1.7.0: execute the following command in your conda env automsr

pip install ray==1.7.0

Running the Experiment

1.Searching the GNN Architecture and Hyper Parameter

python search_main.py

2.Testing the Optimal Model Designed by AutoMSR

python test_main.py

Citing

If you think AutoMSR is useful tool for you, please cite our paper, thank you for your support:

@article{chen2022automsr,
  title={AutoMSR: Auto Molecular Structure Representation Learning for Multi-label Metabolic Pathway Prediction},
  author={Chen, Jiamin and Gao, Jianliang and Lyu, Tengfei and Oloulade, Babatounde Moctard and Hu, Xiaohua},
  journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  number={01},
  pages={1--11},
  year={2022},
  publisher={IEEE Computer Society}
}