虚拟筛选
药物发现
生物制药
高通量筛选
生物信息学
计算机科学
计算生物学
化学
数据科学
纳米技术
组合化学
生物技术
生物
生物化学
基因
材料科学
作者
Nitesh Mani Tripathi,Anupam Bandyopadhyay
标识
DOI:10.1016/j.ejmech.2022.114766
摘要
High-throughput virtual screening (HTVS) is a leading biopharmaceutical technology that employs computational algorithms to uncover biologically active compounds from large-scale collections of chemical compound libraries. In addition, this method often leverages the precedence of screening focused libraries for assessing their binding affinities and improving physicochemical properties. Usually, developing a drug sometimes takes ages, and lessons are learnt from FDA-approved drugs. This screening strategy saves resources and time compared to laboratory testing in certain stages of drug discovery. Yet in-silico investigations remain challenging in some cases of drug discovery. For the last few decades, peptide-based drug discoveries have received remarkable momentum for several advantages over small molecules. Therefore, developing a high-fidelity HTVS platform for chemically versatile peptide libraries is highly desired. This review summarises the modern and frequently appreciated HTVS strategies for peptide libraries from 2011 to 2021. In addition, we focus on the software used for preparing peptide libraries, their screening techniques and shortcomings. An index of various HTVS methods reported here should assist researchers in identifying tools that could be beneficial for their peptide library screening projects.
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