Deep learning methods are a proven commodity in many fields and endeavors. One of these endeavors is predicting the presence of adverse drug–drug interactions (DDIs).
The models generated can predict, with reasonable accuracy, the phenotypes arising from the drug interactions using their molecular structures. Nevertheless, this task requires improvement to be truly useful.
Given the complexity of the predictive task, an extensive benchmarking on structure-based models for DDIs prediction was performed to evaluate their drawbacks and advantages.