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首页> 外文期刊>Briefings in bioinformatics >Network-based drug discovery by integrating systems biology and computational technologies
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Network-based drug discovery by integrating systems biology and computational technologies

机译:整合系统生物学和计算技术的基于网络的药物发现

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摘要

Network-based intervention has been a trend of curing systemic diseases, but it relies on regimen optimization and valid multi-target actions of the drugs. The complex multi-component nature of medicinal herbs may serve as valuable resources for network-based multi-target drug discovery due to its potential treatment effects by synergy. Recently, robustness of multiple systems biology platforms shows powerful to uncover molecular mechanisms and connections between the drugs and their targeting dynamic network.However, optimization methods of drug combination are insufficient, owning to lacking of tighter integration across multiple ‘-omics’ databases. The newly developed algorithm- or network-based computational models can tightly integrate ‘-omics’ databases and optimize combinational regimens of drug development, which encourage using medicinal herbs to develop into new wave of network-based multi-target drugs. However, challenges on further integration across the databases of medicinal herbs with multiple system biology platforms for multi-target drug optimization remain to the uncertain reliability of individual data sets, width and depth and degree of standardization of herbal medicine. Standardization of the methodology and terminology of multiple system biology and herbal database would facilitate the integration. Enhance public accessible databases and the number of research using system biology platform on herbal medicine would be helpful. Further integration across various ‘-omics’ platforms and computational tools would accelerate development of network-based drug discovery and network medicine.
机译:基于网络的干预已成为治疗全身性疾病的一种趋势,但它依赖于药物的方案优化和有效的多靶点作用。由于其潜在的协同作用,药材的复杂的多组分性质可作为基于网络的多目标药物发现的宝贵资源。最近,多种系统生物学平台的强大功能强大,可以揭示药物与其靶向动态网络之间的分子机制和联系。但是,由于缺乏跨多个“组学”数据库的紧密集成,药物组合的优化方法不足。新开发的基于算法或基于网络的计算模型可以紧密集成“ -omics”数据库并优化药物开发的组合方案,从而鼓励使用草药发展为基于网络的多目标药物的新浪潮。但是,在跨草药数据库与多系统生物学平台进行多目标药物优化的进一步集成方面,挑战仍然存在于各个数据集的不确定性,草药的宽度和深度以及标准化程度的不确定性。多系统生物学和草药数据库的方法论和术语的标准化将促进整合。增强公共可访问的数据库,使用系统生物学平台对草药进行研究的数量将是有帮助的。跨各种“组学”平台和计算工具的进一步集成将加速基于网络的药物发现和网络医学的开发。

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