Benchmarks
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Greatest papers with code
GraphEBM: Molecular Graph Generation with Energy-Based Models
• divelab/DIG
• 
We note that most existing approaches for molecular graph generation fail to guarantee the intrinsic property of permutation invariance, resulting in unexpected bias in generative models.
GraphNVP: An Invertible Flow Model for Generating Molecular Graphs
We propose GraphNVP, the first invertible, normalizing flow-based molecular graph generation model.
Junction Tree Variational Autoencoder for Molecular Graph Generation
ICML 2018
• wengong-jin/icml18-jtnn
•
We evaluate our model on multiple tasks ranging from molecular generation to optimization.
Ranked #1 on
Molecular Graph Generation
on InterBioScreen
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
NeurIPS 2018
• bowenliu16/rl_graph_generation
• 
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research.
MoFlow: An Invertible Flow Model for Generating Molecular Graphs
Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process.
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Molecular graph generation is a fundamental problem for drug discovery and has been attracting growing attention.
DENSITY ESTIMATION DRUG DISCOVERY GRAPH GENERATION MOLECULAR GRAPH GENERATION
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars
A major challenge in the pharmaceutical industry is to design novel molecules with specific desired properties, especially when the property evaluation is costly.