  Chemical representations derived from deep learning are emerging as a
powerful tool in areas such as drug discovery and materials innovation.
Currently, this methodology has three major limitations - the cost of
representation generation, risk of inherited bias, and the requirement for
large amounts of data. We propose the use of multi-task learning in tandem with
transfer learning to address these limitations directly. In order to avoid
introducing unknown bias into multi-task learning through the task selection
itself, we calculate task similarity through pairwise task affinity, and use
this measure to programmatically select tasks. We test this methodology on
several real-world data sets to demonstrate its potential for execution in
complex and low-data environments. Finally, we utilise the task similarity to
further probe the expressiveness of the learned representation through a
comparison to a commonly used cheminformatics fingerprint, and show that the
deep representation is able to capture more expressive task-based information.
