Add the DeepDrug Model

[benedekrozemberczki]

Dear @kajocina,

• Please read the paper first. It is here.
• There is also code-release with the paper here.
• After that read the contributing guidelines.
• If there is an existing open-source version of the model please take a look.
• ChemicalX is built on top of PyTorch 1.10. and torchdrug.
• A similar model is EPGCNDS which usesGraphConvolutions to generate drug representations. Take a look at the layer definition here. You should use the layers from torchdrug not the models.
• The library heavily builds on top on torchdrug and molecules in batches are PackedGraphs.
• There is already a model class under ./chemicalx/models/
• Context features, drug level features, and labels are all FloatTensors.
• Look at the examples and tests under ./examples/ and ./tests/.
• Add auxiliary layers as you see fit - please document these, add tests, and add these layers to the main readme.md if needed.
• Add typing to the initialization and forward pass.
• Non-data-dependent hyper-ammeters should have default values.
• Please add tests under ./tests/ and make sure that your model/layer is tested with real data.
• Write an example under ./examples/. What is the AUC on the test set? Is it reasonable?