swMATH ID: 36059
Software Authors: Nicola De Cao, Thomas Kipf
Description: MolGAN: An implicit generative model for small molecular graphs. 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
Homepage: https://arxiv.org/abs/1805.11973
Source Code:  https://github.com/nicola-decao/MolGAN
Keywords: Machine Learning; arXiv_stat.ML; arXiv_cs.LG; small molecular graphs; Deep generative models; generative adversarial networks; GANs
Related Software: DeepWalk; node2vec; NetGAN; GraphRNN; LINE; GraphVAE; GMNN; struc2vec; PointNet; BERT; PyTorch; Tensor2Tensor; RotatE; InfoGraph; CLUTRR; Walklets; NetProbe; STransE; LanczosNet; CayleyNets
Cited in: 5 Documents

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