Progress in molecular docking

被引：301

作者：

Fan, Jiyu ^{[1
]}

Fu, Ailing ^{[2
]}

Zhang, Le ^{[1
,3
,4
]}

机构：

[1] Sichuan Univ, Sch Comp Sci, Chengdu 610065, Sichuan, Peoples R China

[2] Southwest Univ, Coll Pharmaceut Sci, Chongqing 400715, Peoples R China

[3] Sichuan Univ, Med Big Data Ctr, Chengdu 610065, Peoples R China

[4] Chongqqing Zhongdi Med Informat Technol Co Ltd, Chongqing 401320, Peoples R China

来源：

QUANTITATIVE BIOLOGY | 2019年 / 7卷 / 02期

基金：

中国国家自然科学基金;

关键词：

molecular docking; numerical analysis; optimization; data mining; DRUG DISCOVERY; PROTEIN; OPTIMIZATION; PREDICTION; DATABASE; MODEL; ZDOCK;

D O I：

10.1007/s40484-019-0172-y

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

BackgroundIn recent years, since the molecular docking technique can greatly improve the efficiency and reduce the research cost, it has become a key tool in computer-assisted drug design to predict the binding affinity and analyze the interactive mode.ResultsThis study introduces the key principles, procedures and the widely-used applications for molecular docking. Also, it compares the commonly used docking applications and recommends which research areas are suitable for them. Lastly, it briefly reviews the latest progress in molecular docking such as the integrated method and deep learning.ConclusionLimited to the incomplete molecular structure and the shortcomings of the scoring function, current docking applications are not accurate enough to predict the binding affinity. However, we could improve the current molecular docking technique by integrating the big biological data into scoring function.

引用

页码：83 / 89

页数：7

共 44 条

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