I have working with generative modelling for Molecules (SMILES) and I was exploring the
AspuruGuzikAutoEncoder given on
seqtoseq.py. The original paper has a step for Gaussian Process step for exploring the latent space and I couldn't find its implementation in deepchem. It would be really helpful if someone could suggest me generative models research or frameworks which can provide us with the option of exploring the latent space for finding more optimized molecules.
Thanks in advance :)
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