1. I agree that graph convolutions make a lot of sense. However, it is not shown yet that they are better than SMILES, although it might just be a matter of time. A lot of graph variants are possible, or maybe we should look at molecules not as graphs, but as quantum objects (See the other Stanford paper on atom convolutions).
2. Yes, Deepchem is built on top of Tensorflow. This additional layer looks useful, but is it really the case? Using standard NLP models is simpler than using deepchem models, and NLP models might still be the state-of-the-art for chemistry tasks, via SMILES (until we get a reasonably comprehensive benchmark, which MoleculeNet is not).
I am not buying into a chemistry-specific library, until it is shown to be really necessary.