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Graph neural network for predicting HOMO-LUMO energy gaps of molecules

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Bachelor's Thesis Project

This is a study of Graph Neural Networks to predict the HOMO-LUMO energy gap of a molecule given its structure. The RDKit and OGB packages are used to handle molecular features and graph-structured data. Using PyTorch Geometric a graph isomorphism network(GIN) and a graph convolutional network(GCN) are trained

References

[1] Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2019). How powerful are graph neural networks?. ICLR 2019

[2] Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. ICLR 2017

[3] Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. Neural message passing for quantum chemistry. ICML 2017.

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Graph neural network for predicting HOMO-LUMO energy gaps of molecules

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