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MolGAN: An implicit generative model for small molecular graphs

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arxiv 1805.11973 v2 pith:KKS3LVQH submitted 2018-05-30 stat.ML cs.LG

Nicola De Cao , Thomas Kipf This is my paper
classification stat.MLcs.LG
keywords chemicalgenerativegraphsmolgandirectlymodelmoleculardata
verification ladder T0 review T1 audit T2 compute T3 formal
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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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