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Extending molecular scaffolds with building blocks

MoLeR is a graph-based generative model that combines fragment-based and atom-by-atom generation of new molecules with scaffold-constrained optimization. It does not depend on generation history and therefore MoLeR is able to complete arbitrary scaffolds. The model has been trained on the GuacaMol dataset. Here we sample the 300k building blocks library from Enamine.

Identifiers

  • EOS model ID: eos633t
  • Slug: moler-enamine-blocks

Characteristics

  • Input: Compound
  • Input Shape: Single
  • Task: Generative
  • Output: Compound
  • Output Type: String
  • Output Shape: List
  • Interpretation: 1000 new molecules are sampled for each input molecule, preserving its scaffold.

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Ersilia model URLs

Citation

If you use this model, please cite the original authors of the model and the Ersilia Model Hub.

License

This package is licensed under a GPL-3.0 license. The model contained within this package is licensed under a MIT license.

Notice: Ersilia grants access to these models 'as is' provided by the original authors, please refer to the original code repository and/or publication if you use the model in your research.

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The Ersilia Open Source Initiative is a Non Profit Organization (1192266) with the mission is to equip labs, universities and clinics in LMIC with AI/ML tools for infectious disease research.

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