Sign in
BioinformaticsIEEE Transactions on Computational Biology and Bioinformatics
Peer-reviewed journal article

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

Somnath MondaliD, Debarghya DattaiD, Soumajit PramanikiD, Rukmankesh MehraiD

Abstract

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 (Unified Graph Embedding Network for Drug-Drug 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. HighlightsO_LIUniGEN-DDI: A simple unified network model was developed for drug-drug interactions. C_LIO_LINode embeddings were generated using GraphSAGE and Node2Vec algorithms. C_LIO_LIA significantly higher computational efficiency was achieved compared to baselines. C_LIO_LIModel validated in non-overlapping splitting of data targetting multiple body parts. C_LIO_LIUniGEN-DDI estimated unknown interactions verified in the updated DrugBank 6.0. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/615812v1_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@909caaorg.highwire.dtl.DTLVardef@1d7cfdorg.highwire.dtl.DTLVardef@1f8ca34org.highwire.dtl.DTLVardef@a340d6_HPS_FORMAT_FIGEXP M_FIG C_FIG

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISLiu '22Xu '24Wang '24Szydlik '25Qian '19Zhang '26
74% semantic matchBioinformatics
Shichao Liu, Yang Zhang, Yuxin Cui+3 more·IEEE Transactions on Computational Biology and Bioinformatics·May 5, 2022· 114 citations