  Computer-Aided Drug Discovery research has proven to be a promising direction
in drug discovery. In recent years, Deep Learning approaches have been applied
to problems in the domain such as Drug-Target Interaction Prediction and have
shown improvements over traditional screening methods. An existing challenge is
how to represent compound-target pairs in deep learning models. While several
representation methods exist, such descriptor schemes tend to complement one
another in many instances, as reported in the literature. In this study, we
propose a multi-view architecture trained adversarially to leverage this
complementary behavior by integrating both differentiable and predefined
molecular descriptors. We conduct experiments on clinically relevant benchmark
datasets to demonstrate the potential of our approach.
