atom_feature="symbol" is only available for class GCPNGeneration() ?

[teltim]

     ...
    @torch.no_grad()
    def generate(self, num_sample, max_resample=20, off_policy=False, max_step=30 * 2, initial_smiles="C", verbose=0):
        is_training = self.training
        self.eval()
        graph = data.Molecule.from_smiles(initial_smiles, kekulize=True, atom_feature="symbol").repeat(num_sample)
     ...

Is there any use for limiting the graph features during generation to symbols?
Does this mean that generation will not work for anything other than symbols?

Actually, reonforcement_learning can finish in less than a long time for "symbols."
But, it can spend long time with, for instance, "explicit_property_prediction".
I do not know the reason...

[chrisvdwerf]

I noticed that, when splitting up finalized molecular graphs into subgraphs, there is no refeaturization in the torchdrug implementation of GCPN.

For some features this can be troublesome, e.g. number of implicit hydrogen atoms, as these node features depend on neighbourhood of a node.

As such, I think that you should be careful about which features you include in the node embeddings.