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Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

Code for MXMNet proposed in our paper: Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures, which has been accepted by the Machine Learning for Structural Biology Workshop (MLSB 2020) and the Machine Learning for Molecules Workshop (ML4Molecules 2020) at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020).

Important Update about Improved Model (2023/11)

We have released the code for PAMNet in our Nature Scientific Reports paper "A universal framework for accurate and efficient geometric deep learning of molecular systems", which is an improved version of MXMNet with higher accuracy and efficiency. We highly recommend anyone interested in MXMNet try our PAMNet.

Overall Architecture

Requirements

CUDA : 10.1 Python : 3.7.10

The other dependencies can be installed with:

pip install -r requirements.txt

How to Run

You can directly download, preprocess the QM9 dataset and train the model with

python main.py

Optional arguments:

  --gpu             GPU number
  --seed            random seed
  --epochs          number of epochs to train
  --lr              initial learning rate
  --wd              weight decay value
  --n_layer         number of hidden layers
  --dim             size of input hidden units
  --batch_size      batch size
  --target          index of target (0~11) for prediction on QM9
  --cutoff          distance cutoff used in the global layer

The default model to be trained is the MXMNet (BS=128, d_g=5) by using '--batch_size=128 --cutoff=5.0'.

Cite

If you find this model and code are useful in your work, please cite our paper:

@article{zhang2020molecular,
  title={Molecular mechanics-driven graph neural network with multiplex graph for molecular structures},
  author={Zhang, Shuo and Liu, Yang and Xie, Lei},
  journal={arXiv preprint arXiv:2011.07457},
  year={2020}
}