计算机科学
计算生物学
药物发现
对接(动物)
虚拟筛选
生物信息学
机器学习
数据科学
数据挖掘
医学
蛋白质-配体对接
生物
护理部
作者
Surovi Saikia,Manobjyoti Bordoloi
出处
期刊:Current Drug Targets
[Bentham Science]
日期:2018-10-25
卷期号:20 (5): 501-521
被引量:333
标识
DOI:10.2174/1389450119666181022153016
摘要
Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.
科研通智能强力驱动
Strongly Powered by AbleSci AI