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

TL;DR

This paper introduces a Siamese attention-based deep learning model that predicts drug-drug interactions by integrating multiple similarity measures, providing high accuracy and explainability, and validated on benchmark datasets.

Contribution

It presents a novel end-to-end Siamese neural network with attention for DDI prediction, enhancing accuracy and interpretability over existing models.

Findings

Achieves AUPR scores from 0.77 to 0.92 on benchmarks

Provides model explainability through attention mechanisms

Validates novel DDIs with independent data sources

Abstract

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.