lilleswing

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?

DeepChem developer and MoleculeNet co-author here.

We are NOT trying to get vendor lock in for users of DeepChem. If there are complaints about lock in using our tools please file a github issue and we can work on trying to create an open easy to use API for everyone.

Chemception/Chemnet do just that with OK performance. https://arxiv.org/abs/1706.06689 https://arxiv.org/abs/1712.02734

There is a lot of research on attempting to approximate DFT level energy calculations with simple Neural Networks.

https://arxiv.org/abs/1610.08935 https://github.com/deepchem/deepchem/blob/master/deepchem/mo...

http://aip.scitation.org/doi/pdf/10.1063/1.4986081

It is a pretty straight-forward application of ML to the space that is starting to have solid results in practice.

Really cool stuff here, a lot along the same lines of what we have been trying to do with DeepChem and MoleculeNet.

https://deepchem.io/

https://github.com/deepchem/deepchem

http://moleculenet.ai/

We seem to be a little further along and stable than this repo. We are also based on Tensorflow instead of raw cuda which means users can play around with the networks more easily.