The authors use a two-step approach to build a model that accurately predicts the lipophilicity (LogP) of small molecules. First, they train the model on a large amount of low accuracy predicted LogP values and then they fine-tune the network using a small, accurate dataset of 244 druglike compounds. The model achieves an average root mean squared error of 0.988 and 0.715 against druglike molecules from Reaxys and PHYSPROP.
- EOS model ID:
eos9ym3 - Slug:
mrlogp
- Input:
Compound - Input Shape:
Single - Task:
Regression - Output:
Descriptor - Output Type:
Float - Output Shape:
Single - Interpretation: PRedicted LogP of small molecules
- Publication
- Source Code
- Ersilia contributor: leilayesufu
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