可药性
药物发现
鉴定(生物学)
计算生物学
预测(人工智能)
背景(考古学)
天然产物
药物开发
机制(生物学)
炎症
神经退行性变
药物靶点
生物信息学
计算机科学
医学
生物
神经科学
药品
药理学
疾病
免疫学
人工智能
病理
古生物学
植物
生物化学
哲学
认识论
基因
作者
Xian Pan,Shan Jiang,Xinzhuang Zhang,Zhenzhong Wang,Shunyao Wang,Liang Cao,Wei Xiao
摘要
Natural products are a treasure trove for drug discovery, especially in the areas of infection, inflammation and cancer, due to their diverse bioactivities and complex, and varied structures. Chronic inflammation is closely related to many diseases, including complex diseases such as cancer and neurodegeneration. Improving target identification for natural products contributes to elucidating their mechanism of action and clinical progress. It also facilitates the discovery of novel druggable targets and the elimination of undesirable ones, thereby significantly enhancing the productivity of drug discovery and development. Moreover, the rise of polypharmacological strategies, considered promising for the treatment of complex diseases, will further increase the demand for target deconvolution. This review underscores strategies for identifying natural product targets (NPs) in the context of chronic inflammation over the past 5 years. These strategies encompass computational methodologies for early target discovery and the anticipation of compound binding sites, proteomics‐driven approaches for target delineation and experimental biology techniques for target validation and comprehensive mechanistic exploration.
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