The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving the encoding of substructure components with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its attachment to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model significantly outperforms previous state-of-the-art baselines.
We propose a multi-resolution, hierarchically coupled encoder-decoder for graph-to-graph translation. A hierarchical graph-to-graph translation model to generate molecular graphs using chemical substructures as building blocks that is fully autoregressive and learns coherent multi-resolution representations, outperforming previous models. The authors present a hierarchical graph-to-graph translation method for generating novel organic molecules.