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

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Názov: PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery
Autori: Saminur Islam, Ahmed Abbasi, Nitin Agarwal, Wanhong Zheng, Gianfranco Doretto, Donald A. Adjeroh
Zdroj: Scientific Reports, Vol 15, Iss 1, Pp 1-23 (2025)
Informácie o vydavateľovi: Nature Portfolio, 2025.
Rok vydania: 2025
Zbierka: LCC:Medicine
LCC:Science
Predmety: Protien sequence structure similarity network (PS3N), Drug-drug interaction, Deep learning, Similarity network fusion, Medicine, Science
Popis: Abstract 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.
Druh dokumentu: article
Popis súboru: electronic resource
Jazyk: English
ISSN: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-025-14690-w
Prístupová URL adresa: https://doaj.org/article/067b32b1c38444009d3efa40636b0c26
Prístupové číslo: edsdoj.067b32b1c38444009d3efa40636b0c26
Databáza: Directory of Open Access Journals
Popis
Abstrakt:Abstract 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.
ISSN:20452322
DOI:10.1038/s41598-025-14690-w