List of suggested readings and notebooks

Requirements

• PyTorch
• scikit-learn
• RDKit
• Jupyter Notebook

Suggested readings

• Gilmer, Justin, et al. "Neural message passing for quantum chemistry." Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.
• https://towardsdatascience.com/how-to-do-deep-learning-on-graphs-with-graph-convolutional-networks-7d2250723780
• Tsubaki, Masashi, Kentaro Tomii, and Jun Sese. "Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences." Bioinformatics 35.2 (2018): 309-318.
• Zitnik, Marinka, Monica Agrawal, and Jure Leskovec. "Modeling polypharmacy side effects with graph convolutional networks." Bioinformatics 34.13 (2018): i457-i466.