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计算药物设计在癌症治疗中的应用研究。

Computational Studies in Drug Design Against Cancer.

机构信息

Advanced Technology Development Center, Indian Institute of Technology, Kharagpur, India.

Indira College of Pharmacy, Nanded, Maharashtra, India.

出版信息

Anticancer Agents Med Chem. 2019;19(5):587-591. doi: 10.2174/1871520618666180911125700.

Abstract

BACKGROUND

The application of in silico tools in the development of anti cancer drugs.

OBJECTIVE

The summing of different computer aided drug design approaches that have been applied in the development of anti cancer drugs.

METHODS

Structure based, ligand based, hybrid protein-ligand pharmacophore methods, Homology modeling, molecular docking aids in different steps of drug discovery pipeline with considerable saving in time and expenditure. In silico tools also find applications in the domain of cancer drug development.

RESULTS

Structure-based pharmacophore modeling aided in the identification of PUMA inhibitors, structure based approach with high throughput screening for the development of Bcl-2 inhibitors, to derive the most relevant protein-protein interactions, anti mitotic agents; I-Kappa-B Kinase β (IKK- β) inhibitor, screening of new class of aromatase inhibitors that can be important targets in cancer therapy.

CONCLUSION

Application of computational methods in the design of anti cancer drugs was found to be effective.

摘要

背景

计算机辅助药物设计在抗癌药物研发中的应用。

目的

总结已应用于抗癌药物研发的不同计算机辅助药物设计方法。

方法

基于结构的、基于配体的、混合蛋白-配体药效团方法、同源建模、分子对接在药物发现管道的不同步骤中都有应用,极大地节省了时间和开支。计算机辅助药物设计工具在癌症药物研发领域也有应用。

结果

基于结构的药效团模型辅助识别 PUMA 抑制剂,基于结构的高通量筛选开发 Bcl-2 抑制剂,以得出最相关的蛋白-蛋白相互作用,抗有丝分裂剂;I-κB 激酶β(IKK-β)抑制剂,筛选新类别的芳香酶抑制剂,这些抑制剂可能是癌症治疗的重要靶点。

结论

应用计算方法设计抗癌药物是有效的。

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