Guangxi University, Life Science and Biotechnology College, Nanning, Guangxi, 530004 , China.
Expert Opin Drug Discov. 2011 Jun;6(6):619-31. doi: 10.1517/17460441.2011.571671. Epub 2011 Mar 26.
The 2009-H1N1 influenza pandemic has prompted new global efforts to develop new drugs and drug design techniques to combat influenza viruses. While there have been a number of attempts to provide drugs to treat influenza, drug resistance has been a major problem with only four drugs currently approved by the FDA for its treatment.
In this review, the drug-resistant problem of influenza A viruses is discussed and summarized. The article also introduces the experimental and computational structures of drug targeting proteins, neuraminidases, and of the M2 proton channel. Furthermore, the article illustrates the latest drug candidates and techniques of computer-aided drug design with examples of their application, including virtual in silico screening and scoring, AutoDock and evolutionary technique AutoGrow.
Structure-based drug design is the inventive process for finding new drugs based on the structural knowledge of the biological target. Computer-aided drug design strategies and techniques will make drug discovery more effective and economical. It is anticipated that the recent advances in structure-based drug design techniques will greatly help scientists to develop more powerful and specific drugs to fight the next generation of influenza viruses.
2009 年 H1N1 流感大流行促使全球新努力开发新药和药物设计技术来对抗流感病毒。尽管已经有许多尝试提供药物来治疗流感，但耐药性一直是一个主要问题，目前只有四种药物被 FDA 批准用于治疗流感。
本文讨论和总结了甲型流感病毒的耐药性问题。文章还介绍了药物靶蛋白、神经氨酸酶和 M2 质子通道的实验和计算结构。此外，本文通过实例说明了计算机辅助药物设计的最新候选药物和技术，包括虚拟计算机筛选和评分、AutoDock 和进化技术 AutoGrow。
基于生物靶标的结构知识寻找新药的药物设计是一种创造性过程。计算机辅助药物设计策略和技术将使药物发现更加有效和经济。预计基于结构的药物设计技术的最新进展将极大地帮助科学家开发更强大和更具特异性的药物来对抗下一代流感病毒。
