I found this repo for the paper you linked above: https://github.com/HIPS/molecule-autoencoder
It's outside of deepchem, but hope this helps!
I'm looking into featurizing a set of molecules with the
ConvMolFeaturizer. I'm interested in featurizing the chemical environment of the atoms within the molecule so I presume that I'd want to set the
per_atom_fragmentation parameter. In the docs it notes:
This option is typically used in combination with a FlatteningTransformer to split the lists into separate samples.
I can't find any mention of
FlatteningTransformer in the docs, can someone point me somewhere?
per_atom_fragmentationis a new feature so this may be a docs error. Check out the new tutorial at https://github.com/deepchem/deepchem/blob/master/examples/tutorials/Training_a_Normalizing_Flow_on_QM9.ipynb
@OmidTarkhaneh There are layers defined within Deepchem from Keras etc, they can be found here: https://deepchem.readthedocs.io/en/latest/api_reference/layers.html
These can be used like how you would use Keras