Thank you very much, Max! To answer your questions: - As you say, jointly training the autoencoder for reconstruction and prediction accuracy would be easy to

Thank you very much, Max! To answer your questions: - As you say, jointly training the autoencoder for reconstruction and prediction accuracy would be easy to code, but it might be tricky to get the tradeoff right since we usually have relatively little labeled data. A massively multi-task approach probably makes sense here. I'm not sure how hard it will be to get smoothness w.r.t. the latent representation, but the logP result was mildly encouraging. - I agree that it's probably time to bring some of the recent augmented recurrent network architectures to bear on graph decoders. What makes is hard is that it requires differentiating through a cascading series of discrete choices, but people are making progress on this sort of problem. - I do think we need to completely start over on the synthetic accessibility. There are just too many ways for molecules to be weird to try to write an explicit function for it. In fact I'd go so far as to say that this is the 'missing half' of this method. - I view this method as a complement to physics-based simulation. A neural net is almost always going to be cheaper to evaluate than a physical simulation, so it's not a bad way to compile all the simulation results together.