Jeonbuk National University researchers develop DDINet for accurate and scalable drug-drug interaction prediction
Peer-Reviewed Publication
Updates every hour. Last Updated: 21-Apr-2026 14:16 ET (21-Apr-2026 18:16 GMT/UTC)
Drug-drug interactions (DDI) can cause adverse drug reactions during the co-administration of multiple drugs, necessitating accurate and scalable prediction tools. While deep learning models have shown promise recently, most models show poor performance against drugs not encountered during training. Now, researchers have developed a lightweight and scalable model, called DDINet, designed specifically to predict unseen drug interactions. This innovative model achieves superior accuracy in predicting interactions for unseen drugs, with potential for practical deployment.
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