Geometric_Deep_Learning

This repository contains forked code from an implementation of Graph Neural Networks for Quantum Chemistry. My contribution was an LSTM readout function and testing of model hyperparameters. My work is indicated in function descriptions.

My work is in /models/ files: LSTM.py, MessageFunction.py, ReadoutFunction.py

Joshua Greene Summer 2020 Independent Study

Graph Neural Networks for Quantum Chemistry

Implementation and modification of Message Passing Neural Networks as explained in the article proposed by Gilmer et al. [1].

Requirements:

• python 3.5
• pytorch=0.1.12
• networkx=1.11
• tensorboard
• tensorboard_logger
• numpy
• joblib

Setup

Using conda create command to create a conda environment.

$ module add anaconda3/4.2.0
$ conda create -n python-3.5 python=3.5
$ source activate python-3.5

Installation

$ pip install numpy tensorboard tensorboard_logger joblib
$ conda install -c rdkit rdkit 
$ conda install networkx=1.11
$ conda install pytorch=0.1.12 cuda75 -c soumith
$ git clone https://github.com/ifding/graph-neural-networks.git
$ cd graph-neural-networks

Examples

QM9

Download and convert QM9 data set:

$ python3 download_data.py qm9 -p /scratch3/feid/mpnn-data/

Train and test MPNN (default) and MPNNv2 model with GPU (default) or not:

$ python3 main.py --datasetPath /scratch3/feid/mpnn-data/qm9/dsgdb9nsd/

$ python3 main.py --datasetPath /scratch3/feid/mpnn-data/qm9/dsgdb9nsd/ --no-cuda

$ python3 main.py --datasetPath /scratch3/feid/mpnn-data/qm9/dsgdb9nsd/ --model MPNNv2
    
$ python3 main.py --datasetPath /scratch3/feid/mpnn-data/qm9/dsgdb9nsd/ --no-cuda --model MPNNv2

$ python3 main.py --datasetPath /scratch3/feid/mpnn-data/qm9/dsgdb9nsd/ --model MPNNv3
    
$ python3 main.py --datasetPath /scratch3/feid/mpnn-data/qm9/dsgdb9nsd/ --no-cuda --model MPNNv3

Bibliography

• [1] Gilmer et al., Neural Message Passing for Quantum Chemistry, arXiv, 2017.
• [2] Schütt, Kristof T., et al. Quantum-chemical insights from deep tensor neural networks Nature communications 8 (2017): 13890.
• [3] Duvenaud et al., Convolutional Networks on Graphs for Learning Molecular Fingerprints, NIPS, 2015.
• [4] Li et al., Gated Graph Sequence Neural Networks, ICLR, 2016.
• [5] Kipf et al., Semi-Supervised Classification with Graph Convolutional Networks, ICLR, 2017
• [6] Defferrard et al., Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NIPS, 2016.
• [7] Kearnes et al., Molecular Graph Convolutions: Moving Beyond Fingerprints, JCAMD, 2016.