  Complex or co-existing diseases are commonly treated using drug combinations,
which can lead to higher risk of adverse side effects. The detection of
polypharmacy side effects is usually done in Phase IV clinical trials, but
there are still plenty which remain undiscovered when the drugs are put on the
market. Such accidents have been affecting an increasing proportion of the
population (15% in the US now) and it is thus of high interest to be able to
predict the potential side effects as early as possible. Systematic
combinatorial screening of possible drug-drug interactions (DDI) is challenging
and expensive. However, the recent significant increases in data availability
from pharmaceutical research and development efforts offer a novel paradigm for
recovering relevant insights for DDI prediction. Accordingly, several recent
approaches focus on curating massive DDI datasets (with millions of examples)
and training machine learning models on them. Here we propose a neural network
architecture able to set state-of-the-art results on this task---using the type
of the side-effect and the molecular structure of the drugs alone---by
leveraging a co-attentional mechanism. In particular, we show the importance of
integrating joint information from the drug pairs early on when learning each
drug's representation.
