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4 changes: 2 additions & 2 deletions README.md
Original file line number Diff line number Diff line change
Expand Up @@ -76,8 +76,8 @@ The list can be found in the `configs/data/chebi50_graph_properties.yml` file.
python -m chebai fit --trainer=configs/training/default_trainer.yml --trainer.logger=configs/training/csv_logger.yml --model=../python-chebai-graph/configs/model/gnn_res_gated.yml --model.train_metrics=configs/metrics/micro-macro-f1.yml --model.test_metrics=configs/metrics/micro-macro-f1.yml --model.val_metrics=configs/metrics/micro-macro-f1.yml --data=../python-chebai-graph/configs/data/chebi50_graph_properties.yml --data.init_args.batch_size=128 --trainer.accumulate_grad_batches=4 --data.init_args.num_workers=10 --model.pass_loss_kwargs=false --data.init_args.chebi_version=241 --trainer.min_epochs=200 --trainer.max_epochs=200 --model.criterion=configs/loss/bce_weighted.yml
```

## Augmented Graphs

## Augmented Graphs
_See thesis related to this work [here](https://www.uni-osnabrueck.de/fileadmin/informatik/Arbeitsgruppen/Hybride_KI/mt_aditya_khedekar.pdf)_.

Graph Neural Networks (GNNs) often fail to explicitly leverage the chemically meaningful substructures present within molecules (i.e. **functional groups (FGs)**). To make this implicit information explicitly accessible to GNNs, we augment molecular graphs with **artificial nodes** that represent these substructures. The resulting graph are referred to as **augmented graphs**.
> Note: Rings are also treated as functional groups in our work.
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