MolGAN: An implicit generative model for small molecular graphs

30 May 2018  ·  Nicola De Cao, Thomas Kipf ·

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 MolGAN, 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 (GANs) 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 QM9 chemical database, we demonstrate that our model is capable of generating close to 100% valid compounds. MolGAN 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. Code at https://github.com/nicola-decao/MolGAN

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Datasets


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Molecular Graph Generation InterBioScreen MolGAN31 Validity 98.1 # 3
Novelty 94.2 # 2
Uniqueness 10.4 # 3
Molecular Graph Generation InterBioScreen GraphVAE31 Validity 55.7 # 7
Novelty 61.6 # 6
Uniqueness 87.0 # 1

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