Predictions of drug metabolism pathways through CYP 3A4 enzyme by analysing drug-target interactions network graph

Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Pub Date : 2021-11-08 DOI:10.1145/3487351.3490959

M. T. Albrijawi, Amrou Haj Ibrahim, R. Alhajj

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Abstract

The available data of drugs and their targets has increased widely in recent years. Far from the traditional way of studying the drug-target interactions, we propose a network-based computational method to identify new targets for known drugs. In this study, the Stanford Biomedical Network Dataset Collection (BIOSNAP Datasets) is used. A network graph is constructed and analyzed to study the relationship between the drugs and their targets. Different centrality and similarity measures analyses are applied and predict new potential metabolism pathways for five drugs, namely (Wortmannin, Voacamine, Vancomycin, Dactinomycin and Arundic acid) through Cytochrome P450 3A4 enzyme in the liver. The application of network theory to the analysis of this dataset reveals a new significant approach. Finally the molecular docking is performed to confirm the results. Also, the importance of the presented method in drug discovery is highlighted/pointed out.

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通过分析药物-靶标相互作用网络图预测cyp3a4酶的药物代谢途径

近年来，有关药物及其靶点的可用数据已广泛增加。与传统的药物-靶点相互作用研究方法不同，我们提出了一种基于网络的计算方法来识别已知药物的新靶点。本研究使用斯坦福生物医学网络数据集(BIOSNAP Datasets)。构建并分析了网络图，研究了药物与靶点之间的关系。应用不同的中心性和相似性测度分析，通过肝脏细胞色素P450 3A4酶预测五种药物(Wortmannin、Voacamine、万古霉素、放线菌霉素和环亚酸)新的潜在代谢途径。将网络理论应用于该数据集的分析，揭示了一种新的有意义的方法。最后进行分子对接验证。此外，本文还强调了该方法在药物发现中的重要性。

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Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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