  Searching for novel molecular compounds with desired properties is an
important problem in drug discovery. Many existing frameworks generate
molecules one atom at a time. We instead propose a flexible editing paradigm
that generates molecules using learned molecular fragments--meaningful
substructures of molecules. To do so, we train a variational autoencoder (VAE)
to encode molecular fragments in a coherent latent space, which we then utilize
as a vocabulary for editing molecules to explore the complex chemical property
space. Equipped with the learned fragment vocabulary, we propose Fragment-based
Sequential Translation (FaST), which learns a reinforcement learning (RL)
policy to iteratively translate model-discovered molecules into increasingly
novel molecules while satisfying desired properties. Empirical evaluation shows
that FaST significantly improves over state-of-the-art methods on benchmark
single/multi-objective molecular optimization tasks.
