This code is a simpler version (different from the original paper) of our GNN model and its implementation for "Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks (The Journal of Physical Chemistry Letters, 2018)" in PyTorch.

We show an example of the learning curve, which uses a subset of the QM9 dataset (the molecular size is less than 14 atoms), as follows.

This result can be completely reproduced by our code and one command (see "Usage").

• This code is easy-to-use for beginners. The requirement is only PyTorch.
• Preprocessing a dataset and learning a GNN model can be done by only one command, "bash train.sh."
• If you prepare another dataset with the same format as seen in the directory, dataset/QM9/data.txt, you can learn a GNN model with your dataset.

• PyTorch (of course numpy and scipy)

We provide two major scripts in the main directory as follows.

• "preprocessing.py" creates tensor data from original text data (see dataset/QM9/data.txt).
• "train.py" trains a GNN model using the preprocessed data to predict a molecular property.

You can easy to train a GNN model by the following commands.

Clone our repository,

git clone https://github.com/masashitsubaki/molecularGNN_3Dstructure.git

change directory,

cd molecularGNN_3Dstructure/main

and run the bash file for training.

bash train.sh

An image of running on google colaboratory is as follows.

You can also change the model hyperparameters described in train.sh (e.g., the dimensionality, number of hidden layers, and batch size).

Try to learn various GNN models to find your own best model for your dataset!

In the dataset directory, we provide a subset of the QM9 dataset (see dataset/QM9/data.txt), which the format is as follows.

If you prepare a dataset with the same format (any molecular property can be used!), you can learn a GNN model with your dataset.

@article{tsubaki2018fast,
  title={Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks},
  author={Tsubaki, Masashi and Mizoguchi, Teruyasu},
  journal={The journal of physical chemistry letters},
  volume={9},
  number={19},
  pages={5733--5741},
  year={2018},
  publisher={ACS Publications}
}