From a user viewpoint, Deepchem would greatly benefit from being a better team player with lower-level (Tensorflow) or other (Pytorch) frameworks. The pace of research (in NLP in particular) is too fast to make it realistic to port everything in Deepchem without an unreasonable delay.

Does it fit the Deepchem agenda? That's another question ;)

Thanks for the feedback (I love feedback). While that is a good high level goal the devil is in the details of how to make it happen. Here are some doable ideas in the medium term which might help.

1) Tutorial and documentation on how to access the raw Tensorflow graph when using DeepChem

2) Tutorial of combining raw Tensorflow with our existing chemistry specific layers

3) Different documentation quick-start sections for ML practitioners and application practitioners.

4) Better overall documentation of our Chemistry Specific layers.

Would these ideas have made a better first user experience for you?

Yes, especially 2)