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.
- EOS model ID:
eos633t - Slug:
moler-enamine-blocks
- 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.
- Publication
- Source Code
- Ersilia contributor: miquelduranfrigola
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