@article{Cao-2018-ID320,
  title     = {Mol{GAN}: An implicit generative model for small molecular graphs},
  abstract  = {Deep generative models for graph-structured data offer a new angle on the
               problem of chemical synthesis: by optimizing differentiable models that
               directly generate molecular graphs, it is possible to side-step expensive
               search procedures in the discrete and vast space of chemical structures. We
               introduce Mol{GAN}, an implicit, likelihood-free generative model for small
               molecular graphs that circumvents the need for expensive graph matching
               procedures or node ordering heuristics of previous likelihood-based
               methods. Our method adapts generative adversarial networks ({GAN}s) to
               operate directly on graph-structured data. We combine our approach with a
               reinforcement learning objective to encourage the generation of molecules
               with specific desired chemical properties. In experiments on the {QM}9
               chemical database, we demonstrate that our model is capable of generating
               close to 100\% valid compounds. Mol{GAN} compares favorably both to recent
               proposals that use string-based ({SMILES}) representations of molecules and
               to a likelihood-based method that directly generates graphs, albeit being
               susceptible to mode collapse.},
  author    = {Cao, Nicola De and Kipf, Thomas},
  journal   = {},
  year      = {2018},
  month     = {5},
  url       = {http://arxiv.org/abs/1805.11973v1},
  url       = {http://arxiv.org/pdf/1805.11973v1},
  arxiv     = {1805.11973v1},
  keywords  = {stat.{ML}},
  file      = {FULLTEXT:pdfs/000/000/000000320.pdf:PDF}
}

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