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利用化学信息学寻找化学战剂的模拟物。

Using cheminformatics to find simulants for chemical warfare agents.

机构信息

Molecular Sciences and Engineering Team, U.S. Army Natick Soldier Research, Development & Engineering Center, 15 Kansas Street, Natick, MA 01760, USA.

出版信息

J Hazard Mater. 2011 Oct 30;194:85-91. doi: 10.1016/j.jhazmat.2011.07.077. Epub 2011 Aug 5.

DOI:10.1016/j.jhazmat.2011.07.077
PMID:21872989
Abstract

Direct experimentation with chemical warfare agents (CWA) to study important problems such as their permeation across protective barrier materials, decontamination of equipment and facilities, or the environmental transport and fate of CWAs is not feasible because of the obvious toxicity of the CWAs and associated restrictions on their laboratory use. The common practice is to use "simulants," namely, analogous chemicals that closely resemble the CWAs but are less toxic, with the expectation that the results attained for simulants can be correlated to how the CWAs would perform. Simulants have been traditionally chosen by experts, by means of intuition, using similarity in one or more physical properties (such as vapor pressure or aqueous solubility) or in the molecular structural features (such as functional groups) between the stimulant and the CWA. This work is designed to automate the simulant identification process backed by quantitative metrics, by means of chemical similarity search software routinely used in pharmaceutical drug discovery. The question addressed here is: By the metrics of such software, how similar are traditional simulants to CWAs? That is, what is the numerical "distance" between each CWA and its customary simulants in the quantitative space of molecular descriptors? The answers show promise for finding close but less toxic simulants for the ever-increasing numbers of CWAs objectively and fast.

摘要

直接用化学战剂(CWA)进行实验,以研究其穿透防护屏障材料、设备和设施的去污、以及 CWA 在环境中的迁移和归宿等重要问题,是不可行的,因为 CWA 的毒性明显,对其实验室使用也有相关限制。通常的做法是使用“模拟物”,即与 CWA 非常相似但毒性较低的类似化学品,期望获得的模拟物的结果可以与 CWA 的表现相关联。传统上,模拟物是由专家通过直觉选择的,依据的是刺激物和 CWA 之间的一个或多个物理性质(如蒸气压或水溶解度)或分子结构特征(如官能团)的相似性。这项工作旨在通过定量指标,利用药物发现中常用的化学相似性搜索软件,实现模拟物识别过程的自动化。这里要解决的问题是:通过这种软件的指标,传统模拟物与 CWA 的相似程度如何?也就是说,在分子描述符的定量空间中,每个 CWA 与其常用模拟物之间的数字“距离”是多少?这些答案有望快速客观地为不断增加的 CWA 找到毒性较低的类似模拟物。

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