Ezqsar: An R Package for Developing QSAR Models Directly From Structures.

Q2 Pharmacology, Toxicology and Pharmaceutics Open Medicinal Chemistry Journal Pub Date : 2017-11-30 eCollection Date: 2017-01-01 DOI:10.2174/1874104501711010212

Jamal Shamsara

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引用次数: 12

Abstract

Background: Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers.

Method and materials: Ezqsar which is introduced here addresses both the issues. It considers important steps to have a reliable QSAR model. Besides calculation of descriptors using CDK library, highly correlated descriptors are removed, a provided data set is divided to train and test sets, descriptors are selected by a statistical method, statistical parameter for the model are presented and applicability domain is investigated.

Results: Finally, the model can be applied to predict the activities for an extra set of molecules for a purpose of either lead optimization or virtual screening. The performance is demonstrated by an example.

Conclusion: The R package, ezqsar, is freely available via https://github.com/shamsaraj/ezqsar, and it runs on Linux and MS-Windows.

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Ezqsar:一个直接从结构中开发QSAR模型的R包。

背景:定量结构活性关系(Quantitative Structure - Activity Relationship, QSAR)是一种对初学者来说比较困难的计算化学方法，对于经验丰富的研究人员来说更是费时费力。方法和材料:这里介绍的Ezqsar解决了这两个问题。它考虑了建立可靠的QSAR模型的重要步骤。在利用CDK库计算描述符的基础上，去除高度相关的描述符，将给定的数据集划分为训练集和测试集，采用统计方法选择描述符，给出模型的统计参数，研究模型的适用范围。结果:最后，该模型可用于预测一组额外分子的活性，用于先导优化或虚拟筛选。通过实例验证了该方法的性能。结论:R包ezqsar可以通过https://github.com/shamsaraj/ezqsar免费获得，它运行在Linux和MS-Windows上。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Open Medicinal Chemistry Journal Pharmacology, Toxicology and Pharmaceutics-Pharmaceutical Science

CiteScore

4.40

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0.00%

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