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<p>Adverse drug events represent a key challenge in public health, especially concerning drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to this problem have used different types of drug-related information utilizing different machine-learning algorithms to predict potential drug interactions. In this work, we focus on genetic information about the drugs, particularly the protein sequence and protein structure of protein targets in drug interaction networks, to predict potential drug interactions. We collected various drug information like drug-drug interaction (DDI), Drug attributes like drug active ingredients, protein targets, protein sequence, protein structure etc. We proposed a similarity-based Neural Network framework called protein sequence-structure similarity network (PS3N) and used this to predict novel DDI’s. The drug-drug similarities are computed using different categories of drug information based on multiple similarity metrics. Our method outperforms the state-of-the-art and achieves competitive results. Our performance evaluations on different datasets showed the predictive performance as follows: Precision 91%–98%, Recall 90%–96%, F1 Score 86%–95%, Area Under Curve (AUC) 88%–99%, and Accuracy 86%–95%. Our evaluation demonstrates the effectiveness of PS3N in predicting DDI’s, including the clinical significance of some new DDI’s discovered by the model.</p>

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PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月24日

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  • Saminur Islam,
  • Ahmed Abbasi,
  • Nitin Agarwal,
  • Wanhong Zheng,
  • Gianfranco Doretto,
  • Donald A. Adjeroh

摘要

Adverse drug events represent a key challenge in public health, especially concerning drug safety profiling and drug surveillance. Drug-drug interactions represent one of the most popular types of adverse drug events. Most computational approaches to this problem have used different types of drug-related information utilizing different machine-learning algorithms to predict potential drug interactions. In this work, we focus on genetic information about the drugs, particularly the protein sequence and protein structure of protein targets in drug interaction networks, to predict potential drug interactions. We collected various drug information like drug-drug interaction (DDI), Drug attributes like drug active ingredients, protein targets, protein sequence, protein structure etc. We proposed a similarity-based Neural Network framework called protein sequence-structure similarity network (PS3N) and used this to predict novel DDI’s. The drug-drug similarities are computed using different categories of drug information based on multiple similarity metrics. Our method outperforms the state-of-the-art and achieves competitive results. Our performance evaluations on different datasets showed the predictive performance as follows: Precision 91%–98%, Recall 90%–96%, F1 Score 86%–95%, Area Under Curve (AUC) 88%–99%, and Accuracy 86%–95%. Our evaluation demonstrates the effectiveness of PS3N in predicting DDI’s, including the clinical significance of some new DDI’s discovered by the model.