Li Peng, Fu Yingxue, Wang Yonghua
Center of Bioinformatics, College of Life Science, Northwest A and F University, Yang ling, Shaanxi, 712100 China.
Mini Rev Med Chem. 2015;15(8):687-95. doi: 10.2174/1389557515666150219143933.
With the rapid development of high-throughput genomic technologies and the accumulation of genome-wide datasets for human disease, it has been shown that using only reductionistic principles has been difficult to capture the complex biological networks and design rational medication. However, the emerging paradigm of "network based methodology" proposes to harness the power of networks to uncover relationships between various data types of interest for drug discovery. Recent advances include networks that encompass relationships between drugs, disease-related genes, therapeutic targets and diseases. It is shown how network techniques can help in the investigation of the mechanism of action of existing drugs, new molecules, or to identify novel disease genes and targets. We review how these different types of network analysis approaches facilitate drug discovery and their associated challenges. Some representative examples are reviewed to show that network analysis is a powerful, integrated, computational and experimental approach to improve the drug discovery process.
随着高通量基因组技术的迅速发展以及人类疾病全基因组数据集的积累，事实表明仅使用还原论原则很难捕捉复杂的生物网络并设计合理的药物。然而，新兴的“基于网络的方法”范式建议利用网络的力量来揭示药物发现中各种感兴趣的数据类型之间的关系。最近的进展包括涵盖药物、疾病相关基因、治疗靶点和疾病之间关系的网络。展示了网络技术如何有助于研究现有药物、新分子的作用机制，或识别新的疾病基因和靶点。我们回顾了这些不同类型的网络分析方法如何促进药物发现及其相关挑战。回顾了一些代表性例子，以表明网络分析是一种强大的、综合的、计算和实验方法，可改善药物发现过程。
