Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR)

Khan Pathan Mohsin; Roy Kunal

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Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR)

机译：在定量结构 - 活动关系中选择特征选择和学习算法的电流方法（QSAR）

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Introduction: Quantitative structure-activity/property relationships (QSAR/QSPR) are statistical models which quantitatively correlate quantitative chemical structure information (described as molecular descriptors) to the response end points (biological activity, property, toxicity, etc.). Important strategies for QSAR model development and validation include dataset curation, variable selection, and dataset division, selection of modeling algorithms and appropriate measures of model validation.

机译：简介：定量结构 - 活动/性能/性能关系（QSAR / QSPR）是定量将定量化学结构信息（描述为分子描述符）与响应终点（生物活性，性质，毒性等）定量相关的统计模型。 QSAR模型开发和验证的重要策略包括数据集策策，变量选择和数据集划分，选择建模算法以及适当的模型验证度量。

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来源
《Expert opinion on drug discovery》 |2018年第12期|共15页
作者
Khan Pathan Mohsin; Roy Kunal;
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作者单位

NIPER Dept Pharmacoinformat Kolkata India;

Jadavpur Univ Dept Pharmaceut Technol Div Med &

Pharmaceut Chem Drug Theoret &

Cheminformat Lab;

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原文格式 PDF
正文语种 eng
中图分类药学;
关键词
QSAR; feature selection; learning algorithms; validation;

机译：QSAR;特征选择;学习算法;验证;

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1. Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR) [J] . Khan Pathan Mohsin, Roy Kunal Expert opinion on drug discovery . 2018,第7a12期

机译：在定量结构 - 活动关系中选择特征选择和学习算法的电流方法（QSAR）
2. The use of quantitative structure-activity relationships (QSARs) and grouping approaches including read-across and category formation to fill data gaps [J] . Cronin M. T. Toxicology Letters: An International Journal Providing a Forum for Original and Pertinent Contributions in Toxicology Research . 2019,第期

机译：使用定量结构 - 活动关系（QSAR）和分组方法，包括读取和类别形成以填补数据差距
3. Quantitative structure-activity relationships (QSARs) in environmental research. Part II. Molecular orbital approaches to property calculation [J] . Mark A. Warne, Jeremy K. Nicholson Progress in environmental science . 2000,第1期

机译：环境研究中的定量构效关系（QSAR）。第二部分分子轨道性质计算方法
4. Machine learning algorithms used in Quantitative structure-activity relationships studies as new approaches in drug discovery [C] . Mourad Stitou, Hamid Toufik, Mohammed Bouachrine, International Conference on Intelligent Systems and Advanced Computing Sciences . 2019

机译：定量结构与活性关系研究中使用的机器学习算法作为药物发现的新方法
5. Quantitative Structure-Activity Relationship (QSAR) Models for Predicting Organophosphate Inhibition of Acetylcholinesterase in Mouse [D] . Suhagia, Tejaskumar. 2017

机译：定量结构-活性关系（QSAR）模型，用于预测小鼠乙酰胆碱酯酶的有机磷酸酯抑制作用
6. Targeting HIV/HCV Coinfection Using a Machine Learning-Based Multiple Quantitative Structure-Activity Relationships (Multiple QSAR) Method [O] . Yu Wei, Wei Li, Tengfei Du, 2019

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7. Building Highly Reliable Quantitative StructureActivity RelationshipAQ1: Is the expanded name provided for {siComponents}#x201c;QSAR{siComponents}#x201d; correct? Classification Models Using the Rivality Index Neighborhood Algorithm with Feature Selection [O] . -1

机译：建立高度可靠的定量结构假期关系和{sicomponents}＃x201c; qsar {sicomponents}＃x201d提供的扩展名称是{sicomponents}＃x201d;正确的？使用具有特征选择的招生索引邻域算法的分类模型

Current approaches for choosing feature selection and learning algorithms in quantitative structure-activity relationships (QSAR)

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