The model uses a combination of two multilayer perceptron networks (baseline and auxiliar) and an autoencoder-like network to extract natural-product specific fingerprints that outperform traditional methods for molecular representation. The training sets correspond to the coconut database (NP) and the Zinc database (synthetic).
- EOS model ID:
eos6tg8 - Slug:
natural-product-fingerprint
- Input:
Compound - Input Shape:
Single - Task:
Representation - Output:
Descriptor - Output Type:
String - Output Shape:
List - Interpretation: Descriptor of a molecule
- 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 None 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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