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Efficient Learning of Non-Autoregressive Graph Variational Autoencoders for Molecular Graph Generation
Python
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GVAE.py Update GVAE.py Oct 31, 2019
README.md Update README.md Nov 25, 2019
datasets.zip
preprocessing.py Update preprocessing.py Oct 30, 2019
test.py
train.py

README.md

graphvae_approx

Tensorflow implementation of the model described in the paper Efficient Learning of Non-Autoregressive Graph Variational Autoencoders for Molecular Graph Generation

Components

  • preprocessing.py - script for preprocessing data
  • train.py - script for model training
  • test.py - script for model evaluation (molecular graph generation)
  • GVAE.py - model architecture

Dependencies

  • Python
  • TensorFlow
  • RDKit
  • NumPy
  • scikit-learn
  • sparse

Citation

@Article{Kwon2019,
  title={Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation},
  author={Kwon, Youngchun and Yoo, Jiho and Choi, Youn-Suk and Son, Won-Joon and Lee, Dongseon and Kang, Seokho},
  journal={Journal of Cheminformatics},
  volume={11},
  pages={70},
  year={2019},
  doi={10.1186/s13321-019-0396-x}
}
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