  Molecular Representation Learning is essential to solving many drug discovery
and computational chemistry problems. It is a challenging problem due to the
complex structure of molecules and the vast chemical space. Graph
representations of molecules are more expressive than traditional
representations, such as molecular fingerprints. Therefore, they can improve
the performance of machine learning models. We propose SeMole, a method that
augments the Junction Tree Variational Autoencoders, a state-of-the-art
generative model for molecular graphs, with semi-supervised learning. SeMole
aims to improve the accuracy of molecular property prediction when having
limited labeled data by exploiting unlabeled data. We enforce that the model
generates molecular graphs conditioned on target properties by incorporating
the property into the latent representation. We propose an additional
pre-training phase to improve the training process for our semi-supervised
generative model. We perform an experimental evaluation on the ZINC dataset
using three different molecular properties and demonstrate the benefits of
semi-supervision.
