  Assessing drug-target affinity is a critical step in the drug discovery and
development process, but to obtain such data experimentally is both time
consuming and expensive. For this reason, computational methods for predicting
binding strength are being widely developed. However, these methods typically
use a single-task approach for prediction, thus ignoring the additional
information that can be extracted from the data and used to drive the learning
process. Thereafter in this work, we present a multi-task approach for binding
strength prediction. Our results suggest that these prediction can indeed
benefit from a multi-task learning approach, by utilizing added information
from related tasks and multi-task induced regularization.
