Shen Jianhua, Xu Xiaoying, Cheng Feng, Liu Hong, Luo Xiaomin, Shen Jingkang, Chen Kaixian, Zhao Weimin, Shen Xu, Jiang Hualiang
Center for Drug Discovery and Design, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 201203, China.
Curr Med Chem. 2003 Nov;10(21):2327-42. doi: 10.2174/0929867033456729.
Natural products, containing inherently large-scale structural diversity than synthetic compounds, have been the major resources of bioactive agents and will continually play as protagonists for discovering new drugs. However, how to access this diverse chemical space efficiently and effectively is an exciting challenge for medicinal chemists and pharmacologists. While virtual screening, which has shown a great promise in drug discovery, will play an important role in digging out lead (active) compounds from natural products. This review focuses on the strategy of virtual screening based on molecular docking and, with successful examples from our laboratory, illustrates the efficiency of virtual screening in discovering active compounds from natural products. On the other hand, the sequencing of the human genome and numerous pathogen genomes has resulted in an unprecedented opportunity for discovering potential new drug targets. Chemogenomics has appeared as a new technology to initiate target discovery by using active compounds as probes to characterize proteome functions. Natural products are the ideal probes for such research. Binding affinity fingerprint is a powerful chemogenomic descriptor to characterize both small molecules and pharmacologically relevant proteins. Therefore, this review also discusses binding affinity fingerprint strategy for identifying target information from the genomic data by using natural products as the probes.
天然产物比合成化合物具有更大的内在结构多样性，一直是生物活性剂的主要来源，并将继续在新药发现中扮演主角。然而，如何高效且有效地探索这个多样的化学空间，对药物化学家和药理学家来说是一项令人兴奋的挑战。虽然虚拟筛选在药物发现中已展现出巨大潜力，将在从天然产物中挖掘先导（活性）化合物方面发挥重要作用。本综述聚焦于基于分子对接的虚拟筛选策略，并结合我们实验室的成功实例，阐述虚拟筛选在从天然产物中发现活性化合物方面的效率。另一方面，人类基因组和众多病原体基因组的测序为发现潜在的新药物靶点带来了前所未有的机遇。化学基因组学作为一种新技术应运而生，通过使用活性化合物作为探针来表征蛋白质组功能，从而启动靶点发现。天然产物是此类研究的理想探针。结合亲和力指纹是一种强大的化学基因组学描述符，可用于表征小分子和药理学相关蛋白质。因此，本综述还讨论了以天然产物为探针从基因组数据中识别靶点信息的结合亲和力指纹策略。
