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

arXiv: https://arxiv.org/abs/2012.13248 · HTML: https://mlsanity.com/paper/2012.13248/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 7 of 7)

- [A Closer Look at AUROC and AUPRC under Class Imbalance](https://mlsanity.com/paper/2401.06091/) (2024)
- [Exploring QSAR Models for Activity-Cliff Prediction](https://mlsanity.com/paper/2301.13644/) (2023)
- [DDoS: A Graph Neural Network based Drug Synergy Prediction Algorithm](https://mlsanity.com/paper/2210.00802/) (2022)
- [Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction](https://mlsanity.com/paper/2411.13688/) (2024)
- [MD-Syn: Synergistic drug combination prediction based on the multidimensional feature fusion method and attention mechanisms](https://mlsanity.com/paper/2501.07884/) (2025)
- [Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction](https://mlsanity.com/paper/2210.10784/) (2022)
- [KITE-DDI: A Knowledge graph Integrated Transformer Model for accurately predicting Drug-Drug Interaction Events from Drug SMILES and Biomedical Knowledge Graph](https://mlsanity.com/paper/2412.05770/) (2024)

## Abstract (arXiv)

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.