肌萎缩侧索硬化
疾病
生物信息学
亨廷顿病
药物发现
小分子
药物重新定位
神经保护
帕金森病
神经退行性变
计算生物学
药理学
计算机科学
神经科学
生物信息学
药物开发
医学
药效团
对接(动物)
药物靶点
可药性
虚拟筛选
化学
作者
Sheikh Arslan Sehgal,Mirza Ahmed Hammad,Rana Adnan Tahir,Hafiza Nisha Akram,Faheem Ahmad
标识
DOI:10.2174/1570159x16666180315142137
摘要
As the number of elderly persons increases, neurodegenerative diseases are becoming ubiquitous. There is currently a great need for knowledge concerning management of oldage neurodegenerative diseases; the most important of which are: Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis, and Huntington's disease.To summarize the potential of computationally predicted molecules and targets against neurodegenerative diseases.Review of literature published since 1997 against neurodegenerative diseases, utilizing as keywords: in silico, Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis ALS, and Huntington's disease was conducted.Due to the costs associated with experimentation and current ethical law, performing experiments directly on living organisms has become much more difficult. In this scenario, in silico techniques have been successful and have become powerful tools in the search to cure disease. Researchers use the Computer Aided Drug Design pipeline which: 1) generates 3- dimensional structures of target proteins through homology modeling 2) achieves stabilization through molecular dynamics simulation, and 3) exploits molecular docking through large compound libraries. Next generation sequencing is continually producing enormous amounts of raw sequence data while neuroimaging is producing a multitude of raw image data. To solve such pressing problems, these new tools and algorithms are required. This review elaborates precise in silico tools and techniques for drug targets, active molecules, and molecular docking studies, together with future prospects and challenges concerning possible breakthroughs in Alzheimer's, Parkinson's, Amyotrophic Lateral Sclerosis, and Huntington's disease.
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