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抗利什曼原虫和抗锥虫药物的计算机辅助药物设计方法的研究进展。

Advanced in Silico Methods for the Development of Anti- Leishmaniasis and Anti-Trypanosomiasis Agents.

机构信息

LAQV@ REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, Porto 4169-007, Portugal.

出版信息

Curr Med Chem. 2020;27(5):697-718. doi: 10.2174/0929867325666181031093702.

Abstract

Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last decades, different in silico methods have been successfully implemented for supporting the lengthy and expensive drug discovery process. In the current review, we discuss recent advances pertaining to in silico analyses towards lead identification, lead modification and target identification of antileishmaniasis and anti-trypanosomiasis agents. We describe recent applications of some important in silico approaches, such as 2D-QSAR, 3D-QSAR, pharmacophore mapping, molecular docking, and so forth, with the aim of understanding the utility of these techniques for the design of novel therapeutic anti-parasitic agents. This review focuses on: (a) advanced computational drug design options; (b) diverse methodologies - e.g.: use of machine learning tools, software solutions, and web-platforms; (c) recent applications and advances in the last five years; (d) experimental validations of in silico predictions; (e) virtual screening tools; and (f) rationale or justification for the selection of these in silico methods.

摘要

利什曼病和锥虫病主要发生在发展中国家,造成数百万人死亡和伤残调整生命年。这些疾病的治疗选择有限,化疗药物毒性高,以及耐药性的出现,都迫切需要开发新的治疗药物。在过去的几十年中,不同的计算方法已成功地应用于支持漫长而昂贵的药物发现过程。在当前的综述中,我们讨论了针对抗利什曼病和抗锥虫病药物的先导物鉴定、先导物修饰和靶标鉴定的计算分析的最新进展。我们描述了一些重要的计算方法的最新应用,例如 2D-QSAR、3D-QSAR、药效团映射、分子对接等,旨在了解这些技术在设计新型治疗性抗寄生虫药物中的应用。本综述重点介绍:(a)先进的计算药物设计选项;(b)多种方法——例如:使用机器学习工具、软件解决方案和网络平台;(c)过去五年的最新应用和进展;(d)计算预测的实验验证;(e)虚拟筛选工具;以及(f)选择这些计算方法的基本原理或依据。

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