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Background: Drug-drug interactions (DDIs) refer to processes triggered by theadministration of two or more drugs leading to side effects beyond thoseobserved when drugs are administered by themselves. Due to the massive numberof possible drug pairs, it is nearly impossible to experimentally test allcombinations 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 forDDI prediction that integrates multiple drug similarity measures that have beenderived 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 multipleseparate steps, 2) it offers model explainability via an Attention mechanismfor identifying salient input features and 3) it achieves similar or betterprediction performance (AUPR scores ranging from 0.77 to 0.92) compared tostate-of-the-art DDI models when tested on various benchmark datasets. NovelDDI predictions are further validated using independent data resources.  Conclusions: We find that a Siamese multi-modal neural network is able toaccurately predict DDIs and that an Attention mechanism, typically used in theNatural Language Processing domain, can be beneficially applied to aid in DDImodel explainability.

AttentionDDI: Siamese Attention-based Deep Learning method for drug-drug interaction predictions