Hey David, author of the linked repo here. I thought the paper was pretty neat and I'm a fan of the graph convolution work being done by both you and Patrick Riley's and Vijay Pandes' groups. A few questions:

- In the paper you trained the property decoder separately but mention in passing that it could be a good idea to train the whole thing jointly. I haven't implemented the property decoder part yet and it seems like a pretty easy extension to just add that to the same loss function. Where is your thinking on independent vs joint training of the autoencoder/property prediction network now? Do you think non-obvious tricks will be required to get nice smoothness of the latent representation for things more complex than logP?

- Though learning directly to/from SMILES is weird, it's also kind of awesome. Do you think the future is an "inverse weave" module or something of that kind, or going further on learning more complex input representations?

- How do you feel about the synthetic accessibility score you used in the paper overall? Obviously it fell short on carbon ring sizes, but do you think we're close on this or we basically need to start over? What datasets would you think about using for "learning" synthetic accessibility as you imply in your post here?

Thanks! My vague intuition had been that this is a domain where I'd expect physics-based simulation should work better than machine learning but I have to say that the recent literature is changing my perspective.