UniGEN-DDI: Computing drug-drug interactions using a unified graph embedding network

Finding drug-drug interaction is crucial for patient safety and treatment efficacy. Two drugs may show a synergistic effect but may sometimes cause a severe health issue, including lethality. Wet lab studies are often performed to understand such interactions but are limited by cost and time. However, the biochemical data generated can be explored to compute unknown interactions. Here, we developed a computational model named UniGEN-DDI ( Uni fied G raph E mbedding N etwork for D rug- D rug Interaction) for the estimation of interactions between drugs. It is a simple unified network model containing the biochemical information of drug association developed using the compiled data from DrugBank 5.1.0. The feature learning of the drugs was carried out using a combination of GraphSAGE and Node2Vec algorithms, which were found efficient in extracting diverse features. The simple architecture of our model led to a significant reduction in computational time compared to the baselines, while maintaining a high prediction accuracy. The model performed well on the data, which was equally distributed between interacting and non-interacting drugs. As a more challenging evaluation, we performed non-overlapping splitting of the data based on the drug action on different parts of the body, and our model performed well in both interaction estimation and time efficiency. Our model successfully identified the 12 unknown drug interactions in DrugBank 5.1.0, which were updated in DrugBank 6.0.

Highlights

• UniGEN-DDI: A simple unified network model was developed for drug-drug interactions.

• Node embeddings were generated using GraphSAGE and Node2Vec algorithms.

• A significantly higher computational efficiency was achieved compared to baselines.

• Model validated in non-overlapping splitting of data targetting multiple body parts.

• UniGEN-DDI estimated unknown interactions verified in the updated DrugBank 6.0.