  Drug discovery aims to find novel compounds with specified chemical property
profiles. In terms of generative modeling, the goal is to learn to sample
molecules in the intersection of multiple property constraints. This task
becomes increasingly challenging when there are many property constraints. We
propose to offset this complexity by composing molecules from a vocabulary of
substructures that we call molecular rationales. These rationales are
identified from molecules as substructures that are likely responsible for each
property of interest. We then learn to expand rationales into a full molecule
using graph generative models. Our final generative model composes molecules as
mixtures of multiple rationale completions, and this mixture is fine-tuned to
preserve the properties of interest. We evaluate our model on various drug
design tasks and demonstrate significant improvements over state-of-the-art
baselines in terms of accuracy, diversity, and novelty of generated compounds.
