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用于纳米定量构效关系模型的纳米材料结构表征综述。

A review on the structural characterization of nanomaterials for nano-QSAR models.

作者信息

Moncho Salvador, Serrano-Candelas Eva, de Julián-Ortiz Jesús Vicente, Gozalbes Rafael

机构信息

ProtoQSAR S.L., CEEI Valencia, Avda. Benjamin Franklin 12, 46980 Paterna, Spain.

Universitat de València, Facultad de Farmacia, Departamento de Química Física, Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Avda. Vicent Andrés Estellés 0, 46100 Burjassot, Spain.

出版信息

Beilstein J Nanotechnol. 2024 Jul 11;15:854-866. doi: 10.3762/bjnano.15.71. eCollection 2024.

DOI:10.3762/bjnano.15.71
PMID:39015425
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11250003/
Abstract

Quantitative structure-activity relationship (QSAR) models are routinely used to predict the properties and biological activity of chemicals to direct synthetic advances, perform massive screenings, and even to register new substances according to international regulations. Currently, nanoscale QSAR (nano-QSAR) models, adapting this methodology to predict the intrinsic features of nanomaterials (NMs) and quantitatively assess their risks, are blooming. One of the challenges is the characterization of the NMs. This cannot be done with a simple SMILES representation, as for organic molecules, because their chemical structure is complex, including several layers and many inorganic materials, and their size and geometry are key features. In this review, we survey the literature for existing predictive models for NMs and discuss the variety of calculated and experimental features used to define and describe NMs. In the light of this research, we propose a classification of the descriptors including those that directly describe a component of the nanoform (core, surface, or structure) and also experimental features (related to the nanomaterial's behavior, preparation, or test conditions) that indirectly reflect its structure.

摘要

定量构效关系(QSAR)模型通常用于预测化学品的性质和生物活性,以指导合成进展、进行大规模筛选,甚至根据国际法规注册新物质。目前,将这种方法应用于预测纳米材料(NMs)固有特性并定量评估其风险的纳米级QSAR(nano-QSAR)模型正在蓬勃发展。其中一个挑战是纳米材料的表征。对于有机分子,可以用简单的SMILES表示法来完成,但对于纳米材料却不行,因为它们的化学结构复杂,包括多层和许多无机材料,而且它们的尺寸和几何形状是关键特征。在这篇综述中,我们查阅了有关纳米材料现有预测模型的文献,并讨论了用于定义和描述纳米材料的各种计算特征和实验特征。鉴于这项研究,我们提出了一种描述符分类方法,包括直接描述纳米形式的一个组成部分(核心、表面或结构)的描述符,以及间接反映其结构的实验特征(与纳米材料的行为、制备或测试条件相关)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e0b/11250003/08c9122fb240/Beilstein_J_Nanotechnol-15-854-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e0b/11250003/b7272f05a52e/Beilstein_J_Nanotechnol-15-854-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e0b/11250003/4bb418789f35/Beilstein_J_Nanotechnol-15-854-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e0b/11250003/bdaaf57f18b5/Beilstein_J_Nanotechnol-15-854-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e0b/11250003/08c9122fb240/Beilstein_J_Nanotechnol-15-854-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e0b/11250003/b7272f05a52e/Beilstein_J_Nanotechnol-15-854-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e0b/11250003/4bb418789f35/Beilstein_J_Nanotechnol-15-854-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e0b/11250003/bdaaf57f18b5/Beilstein_J_Nanotechnol-15-854-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2e0b/11250003/08c9122fb240/Beilstein_J_Nanotechnol-15-854-g005.jpg

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