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2501.06003
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Learning to generate feasible graphs using graph grammars
10 January 2025
Stefan Mautner
Rolf Backofen
Fabrizio Costa
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ArXiv (abs)
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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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