Corpus ID: 43924638

Constrained Graph Variational Autoencoders for Molecule Design

@inproceedings{Liu2018ConstrainedGV,
  title={Constrained Graph Variational Autoencoders for Molecule Design},
  author={Qi Liu and Miltiadis Allamanis and Marc Brockschmidt and Alexander L. Gaunt},
  booktitle={NeurIPS},
  year={2018}
}
Graphs are ubiquitous data structures for representing interactions between entities. [...] Key Method Our decoder assumes a sequential ordering of graph extension steps and we discuss and analyze design choices that mitigate the potential downsides of this linearization. Experiments compare our approach with a wide range of baselines on the molecule generation task and show that our method is more successful at matching the statistics of the original dataset on semantically important metrics. Furthermore, we…Expand
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