We view molecule optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph. A key challenge is therefore to model diverse translation outputs. Our primary contributions include a junction tree encoder-decoder for learning diverse graph translations along with a novel adversarial training method for aligning distributions of molecules. Diverse output distributions in our model are explicitly realized by low-dimensional latent vectors that modulate the translation process. We evaluate our model on multiple molecule optimization tasks and show that our model outperforms previous state-of-the-art baselines by a significant margin. 

We introduce a graph-to-graph encoder-decoder framework for learning diverse graph translations. Proposes a graph-to-graph translation model for molecule optimization inspired by matched molecular pair analysis. Extension of JT-VAE into the graph to graph translation scenario by adding the latent variable to capture multi-modality and an adversarial regularization in the latent space Proposes a quite complex system, involving many different choices and components, for obtaining chemical compouds with improved properties starting from a given corpora.