Integrating herb effect similarity for network-based herb target prediction

Kuo Yang, Xuezhong Zhou, Runshun Zhang, Baoyan Liu, Lei Lei, Xiaoping Zhang, Hongwei Chu, Changkai Sun, Zhuye Gao, Hao Xu
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引用次数: 4

Abstract

Network pharmacology has become the new approach for drug mechanism research and novel drug design. Drug target prediction based on computational approach became one of the primary approaches. However, due to the diversity and complexity of herbal chemical structures, the performance of herb target prediction based on chemical structure similarity is limited by the quality and the data availability of herb chemical ingredients and their structural properties. To gain insights into the molecular mechanism of herbs by using clinical herb efficacies, we develop a computational approach to predict the potential targets of herbs by integrating the herb effect properties. We found that herbs with high effect similarities have high degree of shared targets. Meanwhile, an algorithm integrating propagation on protein-protein interaction network and effect-based herb similarity was proposed and obtained better accuracy than the chemical structure similarity. Furthermore, we manually evaluated some novel predictions like the target SP1 for herb turmeric, which is not recorded in the benchmark set, but has been confirmed by recent published paper.
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基于网络的中药药效相似度综合预测
网络药理学已成为药物机制研究和新药设计的新途径。基于计算方法的药物靶点预测成为主要方法之一。然而,由于中草药化学结构的多样性和复杂性,基于化学结构相似性的中草药目标预测的性能受到中草药化学成分及其结构性质的质量和数据可用性的限制。为了利用临床中药药效来深入了解中药的分子机制,我们开发了一种综合中药药效特性来预测中药潜在靶点的计算方法。我们发现,具有高功效相似性的草药具有高度的共同靶点。同时,提出了一种结合蛋白质-蛋白质相互作用网络传播和基于效果的草本相似度的算法,获得了比化学结构相似度更好的准确性。此外,我们还手动评估了一些新的预测,如草药姜黄的目标SP1,这在基准集中没有记录,但最近发表的论文已经证实了这一点。
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