  Background: Drug-drug interactions (DDIs) refer to processes triggered by the
administration of two or more drugs leading to side effects beyond those
observed when drugs are administered by themselves. Due to the massive number
of possible drug pairs, it is nearly impossible to experimentally test all
combinations and discover previously unobserved side effects. Therefore,
machine learning based methods are being used to address this issue.
  Methods: We propose a Siamese self-attention multi-modal neural network for
DDI prediction that integrates multiple drug similarity measures that have been
derived from a comparison of drug characteristics including drug targets,
pathways and gene expression profiles.
  Results: Our proposed DDI prediction model provides multiple advantages: 1)
It is trained end-to-end, overcoming limitations of models composed of multiple
separate steps, 2) it offers model explainability via an Attention mechanism
for identifying salient input features and 3) it achieves similar or better
prediction performance (AUPR scores ranging from 0.77 to 0.92) compared to
state-of-the-art DDI models when tested on various benchmark datasets. Novel
DDI predictions are further validated using independent data resources.
  Conclusions: We find that a Siamese multi-modal neural network is able to
accurately predict DDIs and that an Attention mechanism, typically used in the
Natural Language Processing domain, can be beneficially applied to aid in DDI
model explainability.
