Suppr超能文献

用于预测香精油熏蒸活性的定量构效关系模型。

QSAR models for the fumigant activity prediction of essential oils.

作者信息

Duchowicz Pablo R, Bennardi Daniel O, Ortiz Erlinda V, Comelli Nieves C

机构信息

Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicadas (INIFTA), CONICET, UNLP, Diag. 113 y 64, C.C. 16, Sucursal 4, 1900, La Plata, Argentina.

Cátedra de Química Orgánica, Facultad de Ciencias Agrarias y Forestales, UNLP, 60 y 119, 1900, La Plata, Argentina.

出版信息

J Mol Graph Model. 2020 Dec;101:107751. doi: 10.1016/j.jmgm.2020.107751. Epub 2020 Sep 9.

Abstract

The Quantitative Structure-Activity Relationships (QSAR) theory, which allows predicting the insecticidal activity of chemical compounds through calculations from the molecular structure, is applied on 23 essential oils composed of 402 structurally diverse compounds at different chemical compositions. A large number of 114,871 conformation-independent molecular descriptors are computed through different types of freely available open-source programs. Mixture descriptors are calculated based on molecular descriptors of the essential oil components and their composition. The best resulting three-descriptor linear regression models are established through the Replacement Method variable subset selection approach. The results obtained in the present work are interesting for predicting the fumigant activity of these essential oil complex mixtures, by means of simple non-conformational QSAR models.

摘要

定量构效关系(QSAR)理论可通过分子结构计算预测化合物的杀虫活性,该理论应用于23种不同化学成分的精油,这些精油由402种结构多样的化合物组成。通过不同类型的免费开源程序计算了大量114,871个与构象无关的分子描述符。基于精油成分的分子描述符及其组成计算混合描述符。通过替换法变量子集选择方法建立了最佳的三描述符线性回归模型。通过简单的非构象QSAR模型,本研究所得结果对于预测这些精油复杂混合物的熏蒸活性很有意义。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验