Computational chemical biology and drug design: Facilitating protein structure, function, and modulation studies

被引:39
作者
Zheng, Mingyue [1 ]
Zhao, Jihui [1 ]
Cui, Chen [1 ]
Fu, Zunyun [1 ]
Li, Xutong [1 ]
Liu, Xiaohong [1 ,2 ]
Ding, Xiaoyu [1 ]
Tan, Xiaoqin [1 ]
Li, Fei [1 ,3 ]
Luo, Xiaomin [1 ]
Chen, Kaixian [1 ,2 ]
Jiang, Hualiang [1 ]
机构
[1] Chinese Acad Sci, Shanghai Inst Mat Med, Drug Discovery & Design Ctr, State Key Lab Drug Res, Shanghai 201203, Peoples R China
[2] ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China
[3] Shanghai Univ, Dept Chem, Coll Sci, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
ADMET; chemical biology; drug design; in silico; lead optimization; molecular dynamics; polypharmacology; virtual screening; IN-SILICO PREDICTION; GENERAL FORCE-FIELD; MOLECULAR-DYNAMICS SIMULATIONS; BINDING-AFFINITY PREDICTION; MACHINE LEARNING TECHNIQUES; CSAR BENCHMARK EXERCISE; SENSING ION CHANNELS; INHALED NITRIC-OXIDE; CYP-MEDIATED SITES; FREE-ENERGY;
D O I
10.1002/med.21483
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
Over the past quarter of a century, there has been rapid development in structural biology, which now can provide solid evidence for understanding the functions of proteins. Concurrently, computational approaches with particular relevance to the chemical biology and drug design (CBDD) field have also incrementally and steadily improved. Today, these methods help elucidate detailed working mechanisms and accelerate the discovery of new chemical modulators of proteins. In recent years, integrating computational simulations and predictions with experimental validation has allowed for more effective explorations of the structure, function and modulation of important therapeutic targets. In this review, we summarize the main advancements in computational methodology development, which are then illustrated by several successful applications in CBDD. Finally, we conclude with a discussion of the current major challenges and future directions in the field.
引用
收藏
页码:914 / 950
页数:37
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