Neural Message Passing for Quantum Chemistry

Implementation of different models of Neural Networks on graphs as explained in the article proposed by Gilmer et al. [1].

Installation

$ pip install -r requirements.txt
$ python main.py

Installation of rdkit

Running any experiment using QM9 dataset needs installing the rdkit package, which can be done following the instructions available here

Data

The data used in this project can be downloaded here.

Bibliography

• [1] Gilmer et al., Neural Message Passing for Quantum Chemistry, arXiv, 2017.
• [2] Duvenaud et al., Convolutional Networks on Graphs for Learning Molecular Fingerprints, NIPS, 2015.
• [3] Li et al., Gated Graph Sequence Neural Networks, ICLR, 2016.
• [4] Battaglia et al., Interaction Networks for Learning about Objects, NIPS, 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.
• [8] Bruna et al., Spectral Networks and Locally Connected Networks on Graphs, ICLR, 2014.

Cite

@Article{Gilmer2017,
  author  = {Justin Gilmer and Samuel S. Schoenholz and Patrick F. Riley and Oriol Vinyals and George E. Dahl},
  title   = {Neural Message Passing for Quantum Chemistry},
  journal = {CoRR},
  year    = {2017}
}

Authors

• Pau Riba (@priba) Webpage
• Anjan Dutta (@AnjanDutta) Webpage