Based on a molecule's pharmacophore, this model generates new molecules de-novo to match the pharmacophore. Internally, pharmacophore hypotheses are generated for a given ligand. A graph neural network encodes spatially distributed chemical features and a transformer decoder generates molecules.
- EOS model ID:
eos69e6 - Slug:
pgmg-pharmacophore-generative
- Input:
Compound - Input Shape:
Single - Task:
Generative - Output:
Compound - Output Type:
String - Output Shape:
List - Interpretation: Model generates new molecules from input molecule by first creating pharmacophore hypotheses and then constraining generation.
- Publication
- Source Code
- Ersilia contributor: miquelduranfrigola
If you use this model, please cite the original authors of the model and the Ersilia Model Hub.
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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