DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction

Abstract

Background: Drug-drug interactions (DDIs) can result in unexpected pharmacological outcomes, including adverse drug events, which are crucial for drug discovery. Graph neural networks have substantially advanced our ability to model molecular representations; however, the precise identification of key local structures and the capture of long-distance structural correlations for better DDI prediction and interpretation remain significant challenges.

Results: Here, we present DrugDAGT, a dual-attention graph transformer framework with contrastive learning for predicting multiple DDI types. The dual-attention graph transformer incorporates attention mechanisms at both the bond and atomic levels, thereby enabling the integration of short and long-range dependencies within drug molecules to pinpoint key local structures essential for DDI discovery. Moreover, DrugDAGT further implements graph contrastive learning to maximize the similarity of representations across different views for better discrimination of molecular structures. Experiments in both warm-start and cold-start scenarios demonstrate that DrugDAGT outperforms state-of-the-art baseline models, achieving superior overall performance. Furthermore, visualization of the learned representations of drug pairs and the attention map provides interpretable insights instead of black-box results.

Conclusions: DrugDAGT provides an effective tool for accurately predicting multiple DDI types by identifying key local chemical structures, offering valuable insights for prescribing medications, and guiding drug development. All data and code of our DrugDAGT can be found at https://github.com/codejiajia/DrugDAGT .

Keywords: Attention; Drug-drug interactions; Graph transformer; Interpretation.

Fig. 1

Overview of the proposed DrugDAGT methodology. DrugDAGT first encodes drug pairs from both training and testing datasets into molecular graph embeddings using a dual-attention graph transformer to capture local structural features. It then processes these features to learn local interactions, thereby enhancing the drug representations via graph contrastive learning. Finally, the FFN module decodes these enhanced representations to predict DDI probabilities in both warm-start and cold-start scenarios

Fig. 2

Performance comparison across 86 DDI types in the DrugBank dataset. A Distribution of drug pair counts and predicted AUPR values for 86 DDI types in the DrugBank dataset. The left vertical axis corresponds to the blue bars representing the range of drug pair counts, displayed using logarithmic scaling for a balanced visualization across all categories. The right vertical axis corresponds to the red line graph showing the range of AUPR values. Due to space constraints, only odd-numbered category labels are displayed on the horizontal axis to prevent clutter. B Influence of hyperparameter message passing steps (T), hidden feature dimension (D), and dropout probability (P) on the performance metrics: accuracy (ACC), precision (PRE), recall (REC), F1-score (F1), and area under the precision-recall curve (AUPR). The scatter plots display results from ten experiments, and the bar graphs show group averages. C Performance comparison of our model against the suboptimal models SA-DDI and Molomer across 86 DDI types. D Comprehensive analysis of F1 and AUPR scores for 1706 drugs in the DrugBank dataset, with light red lines representing F1 and dark red lines indicating AUPR, respectively

Fig. 3

Performance evaluation under multiple scenarios. A Performance comparison of the proposed DrugDAGT with eight baselines and variants under the warm-start scenario. B Performance comparison between the warm-start and cold-start scenarios. C Performance comparison of DrugDAGT with the suboptimal method SA-DDI on the DrugBank dataset with a positive-to-negative sample ratio of 1:5

Fig. 4

Visualization of drug pairs representations and local structures. A t-SNE visualization of drug pair representations learned during training. NMI and ARI are used to evaluate clustering performance. B Visualization of the key local structures for ketoconazole and loxoprofen with five other drugs. In the attention maps, atoms with positive impacts are shown in green, while those with negative impacts are highlighted in red. The darker the color, the stronger the impact. The Tables above each map provide a detailed list of the functional groups and DDI types for ketoconazole and loxoprofen with five other drugs. C Visualization of the key local structures for the SARS-CoV-2 drug combinations. “P” represents the predicted DDI probability generated by DrugDAGT

Fig. 5

Drug representation and graph embedding. A Tranylcypromine graph representation using RDKit. B The message passing and readout phases in graph embedding