TorchDrug property optimization support

[jannisborn]

Currently, our TorchDrug interface does support property optimization conceptually.

However, most runs will fail due to an underlying bug in TorchDrug that raises whenever there is only invalid SMILES in a batch, see https://github.com/DeepGraphLearning/torchdrug/issues/83

Once this is fixed in torchdrug and a new version is released on conda, we can enable our unittests (already written): https://github.com/gt4sd/gt4sd-core/blob/master/src/gt4sd/training_pipelines/tests/test_training_torchdrug_gcpn.py#L76

[jannisborn]

Update: Updated to 0.1.3 and enabled the tests. They work fine but the inference pipelines are now failing with:

raise ValueError(
"Expect node attribute `%s` to have shape (%d, *), but found %s"
% (key, self.num_node, value.shape)
ValueError: Expect node attribute `atom_feature` to have shape (8, *), but found torch.Size([4, 18])

which never occurs in the first but only in the second iteration. Not sure what's going wrong but it's not because the models were trained on 0.1.2. I trained a dummy model on 0.1.3 and it has the same problem when used for inference.

[jannisborn]

Moving forward, current issue was fixed in https://github.com/DeepGraphLearning/torchdrug/issues/117.

A new issue was arising when models generated only invalid molecules, I wrote a PR to fix, see: https://github.com/DeepGraphLearning/torchdrug/issues/125