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Graph neural networks for conditional de novo drug design

https://doi.org/10.1002/wcms.1651
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Transformer-Based Generative Model Accelerating the Development of Novel BRAF Inhibitors
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Graph networks for molecular design
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MoFlow: An Invertible Flow Model for Generating Molecular Graphs
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GF-VAE
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Long Short-Term Memory
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Multi-objective de novo drug design with conditional graph generative model
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Randomized SMILES strings improve the quality of molecular generative models
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Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation
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On random graphs. I.

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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