Learning to generate feasible graphs using graph grammars

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Learning to generate feasible graphs using graph grammars

10 January 2025

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Papers citing "Learning to generate feasible graphs using graph grammars"

6 / 6 papers shown

Title
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models Daniil Polykovskiy Alexander Zhebrak Benjamín Sánchez-Lengeling Sergey Golovanov Oktai Tatanov ... Simon Johansson Hongming Chen Sergey I. Nikolenko Alán Aspuru-Guzik Alex Zhavoronkov ELM 299 662 0 29 Nov 2018
Fréchet ChemNet Distance: A metric for generative models for molecules in drug discovery Kristina Preuer Philipp Renz Thomas Unterthiner Sepp Hochreiter Günter Klambauer MedIm 107 346 0 26 Mar 2018
Learning Deep Generative Models of Graphs Yujia Li Oriol Vinyals Chris Dyer Razvan Pascanu Peter W. Battaglia GNN AI4CE 208 663 0 08 Mar 2018
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models Jiaxuan You Rex Ying Xiang Ren William L. Hamilton J. Leskovec GNN BDL 153 854 0 24 Feb 2018
GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders M. Simonovsky N. Komodakis GNN BDL 159 859 0 09 Feb 2018
Application of generative autoencoder in de novo molecular design T. Blaschke Marcus Olivecrona Ola Engkvist J. Bajorath Hongming Chen AI4CE 119 345 0 21 Nov 2017

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