AttentionDDI: Siamese attention-based deep learning method for drug–drug interaction predictions

• Main Authors: Allam, A. (Author), Krauthammer, M. (Author), Perez Gonzalez, N.A (Author), Schwarz, K. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: adverse drug reaction; Attention; Attention mechanisms; Benchmark datasets; Benchmarking; Deep learning; Deep Learning; drug; drug interaction; Drug interactions; Drug Interactions; Drug–drug interactions; Drug-Related Side Effects and Adverse Reactions; Forecasting; Gene expression; Gene expression profiles; human; Humans; Interaction prediction; Learning systems; Multi-modal neural networks; NAtural language processing; Natural language processing systems; Neural networks; Neural Networks, Computer; Pharmaceutical Preparations; Prediction; Prediction performance; Predictive analytics; Side effects; Similarity measure
• Online Access: View Fulltext in Publisher

 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. © 2021, The Author(s).