Designing Random Graph Models Using Variational Autoencoders With Applications to Chemical Design

By Bidisha Samanta, Abir De, Niloy Ganguly, Manuel Gomez-Rodriguez. 2018

52 citations recorded by the source corpus.

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Citations in the corpus

Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (2018, methodology): “…task through generative modeling (G´omez-Bombarelli et al., 2016; Segler et al., 2017; Kusner et al., 2017; Dai et al., 2018; Jin et al., 2018; Samanta et al., 2018; Li et al., 2018a) and reinforcement learning (Guimaraes et al., 2017; Olivecrona et al., 2017; Popova et al., 2018; You et…”
Graphite: Iterative Generative Modeling of Graphs (2018, background): “Latent variable generative models have also been proposed for generating small molecular graphs (Jin et al., 2018; Samanta et al., 2018; Simonovsky & Komodakis, 2018).”
MolGAN: An implicit generative model for small molecular graphs (2018, methodology): “Evaluation measures We measure the following statistics as defined in Samanta et al. (2018): validity, novelty, and uniqueness.”
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation (2020, background): “2 RELATED WORK A variety of deep generative models have been proposed for molecular graph generation recently (Segler et al., 2017; Olivecrona et al., 2017; Samanta et al., 2018; Neil et al., 2018).”
Constrained Graph Variational Autoencoders for Molecule Design (2018, methodology): “Note that for molecule design, some correlations take the form of known hard rules governing molecule stability, and we explicitly enforce these rules wherever possible using a technique that masks out choices leading to illegal graphs [18, 28].”
Generative Code Modeling with Graphs (2018, background): “This is fundamentally different from sequential generative models of graphs [15, 22], which aim to generate all edges and nodes, whereas our graphs are deterministic augmentations of generated trees.”
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings (2020, background): “Recent years have seen a significantly growing amount of interest in graph neural networks (GNNs), especially on efforts devoted to developing more effective GNNs for node classification [27, 34, 16, 49], graph classification [57, 41] and graph generation [45, 35, 58].”