Graph Energy-based Model for Molecular Graph Generation
We present Graph Energy-based Model (GEM), an energy-based model for molecular graph generation. GEM uses dequantization and gradient symmetrization to incorporate generation by stochastic gradient Langevin dynamics for graph representation that is discrete and includes symmetric constraint. Experimental results show that \gem can comparably design compounds as other deep generative approaches.
PDF AbstractDatasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here