药物发现
生物信息学
计算机科学
过程(计算)
生化工程
数据科学
计算生物学
药物开发
药物设计
分子动力学
药品
纳米技术
风险分析(工程)
生物信息学
化学
生物
医学
药理学
工程类
计算化学
材料科学
操作系统
基因
生物化学
作者
Stephen Fox,Jianguo Li,Yaw Sing Tan,Minh N. Nguyen,Arumay Pal,Zohra Ouaray,Shilpa Yadahalli,Srinivasaraghavan Kannan
标识
DOI:10.2174/1381612822666160425120507
摘要
Discovery of new therapeutics is a very challenging, expensive and time-consuming process. With the number of approved drugs declining steadily, combined with increasing costs, a rational approach is needed to facilitate, expedite and streamline the drug discovery process. In silico methods are playing key roles in the discovery of a growing number of marketed drugs. The use of computational approaches, particularly molecular dynamics, in drug design is rapidly gaining momentum and acceptance as an essential part of the toolkit for modern drug discovery. From analysing atomistic details for explaining experimentally observed phenomena, to designing drugs with increased efficacy and specificity, the insight that such simulations can provide is generating new ideas and applications that have previously been unexplored. Here we discuss physics-based simulation methodologies and applications in drug design: from locating pockets to designing novel lead compounds, from small molecules to peptides. With developments in hardware, software and theory, the improved predictive abilities of in silico efforts are becoming an essential part of efficient, economic and accurate drug development strategies.
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