  Collecting labeled data for many important tasks in chemoinformatics is time
consuming and requires expensive experiments. In recent years, machine learning
has been used to learn rich representations of molecules using large scale
unlabeled molecular datasets and transfer the knowledge to solve the more
challenging tasks with limited datasets. Variational autoencoders are one of
the tools that have been proposed to perform the transfer for both chemical
property prediction and molecular generation tasks. In this work we propose a
simple method to improve chemical property prediction performance of machine
learning models by incorporating additional information on correlated molecular
descriptors in the representations learned by variational autoencoders. We
verify the method on three property prediction asks. We explore the impact of
the number of incorporated descriptors, correlation between the descriptors and
the target properties, sizes of the datasets etc. Finally, we show the relation
between the performance of property prediction models and the distance between
property prediction dataset and the larger unlabeled dataset in the
representation space.
