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Found 6 papers, 5 papers with code
Classifier-free graph diffusion for molecular property targeting
This work focuses on the task of property targeting: that is, generating molecules conditioned on target chemical properties to expedite candidate screening for novel drug and materials development.
GraphGen-Redux: a Fast and Lightweight Recurrent Model for labeled Graph Generation
Several approaches have been proposed in the literature, most of which require to transform the graphs into sequences that encode their structure and labels and to learn the distribution of such sequences through an auto-regressive generative model.
A Deep Generative Model for Fragment-Based Molecule Generation
Molecule generation is a challenging open problem in cheminformatics.
Edge-based sequential graph generation with recurrent neural networks
Graph generation with Machine Learning is an open problem with applications in various research fields.
A Gentle Introduction to Deep Learning for Graphs
The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community.
A Fair Comparison of Graph Neural Networks for Graph Classification
We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.
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Graph Classification
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