An alignment-independent three-dimensional quantitative structure-activity relationship study on ron receptor tyrosine kinase inhibitors.

IF 0.9 4区 生物学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Omid Zarei, Stéphane L Raeppel, Maryam Hamzeh-Mivehroud
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引用次数: 0

Abstract

Recepteur d'Origine Nantais known as RON is a member of the receptor tyrosine kinase (RTK) superfamily which has recently gained increasing attention as cancer target for therapeutic intervention. The aim of this work was to perform an alignment-independent three-dimensional quantitative structure-activity relationship (3D QSAR) study for a series of RON inhibitors. A 3D QSAR model based on GRid-INdependent Descriptors (GRIND) methodology was generated using a set of 19 compounds with RON inhibitory activities. The generated 3D QSAR model revealed the main structural features important in the potency of RON inhibitors. The results obtained from the presented study can be used in lead optimization projects for designing of novel compounds where inhibition of RON is needed.

铁受体酪氨酸激酶抑制剂的三维定量构效关系研究。
受体d'Origine nantai被称为RON,是受体酪氨酸激酶(RTK)超家族的一员,近年来作为癌症治疗干预的靶点受到越来越多的关注。本研究的目的是对一系列RON抑制剂进行不依赖于比对的三维定量构效关系(3D QSAR)研究。利用19个具有RON抑制活性的化合物,建立了基于网格独立描述符(GRIND)方法的QSAR三维模型。生成的3D QSAR模型揭示了影响RON抑制剂效力的主要结构特征。本研究的结果可用于设计需要抑制RON的新化合物的先导优化项目。
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来源期刊
Journal of Bioinformatics and Computational Biology
Journal of Bioinformatics and Computational Biology MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
2.10
自引率
0.00%
发文量
57
期刊介绍: The Journal of Bioinformatics and Computational Biology aims to publish high quality, original research articles, expository tutorial papers and review papers as well as short, critical comments on technical issues associated with the analysis of cellular information. The research papers will be technical presentations of new assertions, discoveries and tools, intended for a narrower specialist community. The tutorials, reviews and critical commentary will be targeted at a broader readership of biologists who are interested in using computers but are not knowledgeable about scientific computing, and equally, computer scientists who have an interest in biology but are not familiar with current thrusts nor the language of biology. Such carefully chosen tutorials and articles should greatly accelerate the rate of entry of these new creative scientists into the field.
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