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 a fragment library from Enamine.
- EOS model ID:
eos9taz - Slug:
moler-enamine-fragments
- 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: anamika-yadav99
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