Corpus ID: 236087674

Analysis of training and seed bias in small molecules generated with a conditional graph-based variational autoencoder - Insights for practical AI-driven molecule generation

@article{Kang2021AnalysisOT,
  title={Analysis of training and seed bias in small molecules generated with a conditional graph-based variational autoencoder - Insights for practical AI-driven molecule generation},
  author={Seung-gu Kang and Joseph A Morrone and Jeffrey K. Weber and Wendy D. Cornell},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.08987}
}
The application of deep learning to generative molecule design has shown early promise for accelerating lead series development. However, questions remain concerning how factors like training, dataset, and seed bias impact the technology’s utility to medicinal and computational chemists. In this work, we analyze the impact of seed and training bias on the output of an activity-conditioned graph-based variational autoencoder (VAE). Leveraging a massive, labeled dataset corresponding to the… Expand

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