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Update page.mdx #39

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Jan 20, 2025
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Update page.mdx #39

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13 changes: 6 additions & 7 deletions .../(posts)/building-a-molecular-graph-neural-network-model-with-jaqpotpy/page.mdx
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Expand Up @@ -58,13 +58,12 @@ featurizer.set_default_config()
# Create datasets
train_dataset = SmilesGraphDataset(
smiles=train_smiles, y=train_y, featurizer=featurizer
).precompute_featurization()
val_dataset = SmilesGraphDataset(
smiles=val_smiles, y=val_y, featurizer=featurizer
).precompute_featurization()
test_dataset = SmilesGraphDataset(
smiles=test_smiles, y=test_y, featurizer=featurizer
).precompute_featurization()
)
train_dataset.precompute_featurization()
val_dataset = SmilesGraphDataset(smiles=val_smiles, y=val_y, featurizer=featurizer)
val_dataset.precompute_featurization()
test_dataset = SmilesGraphDataset(smiles=test_smiles, y=test_y, featurizer=featurizer)
test_dataset.precompute_featurization()
```
### 3. Define the model
Set up the Graph Neural Network model. In our case we will use a simple Graph Convolution Neural Network. For simplicity we will not mess with the network's hyperparameters. However you can freely choose the depth, activation function and more components of the architecture.
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