  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
