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首页> 外文期刊>Chemosphere >Predicting physical properties of emerging compounds with limited physical and chemical data: QSAR model uncertainty and applicability to military munitions
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Predicting physical properties of emerging compounds with limited physical and chemical data: QSAR model uncertainty and applicability to military munitions

机译：物理和化学数据有限的情况下，预测新兴化合物的物理性质：QSAR模型的不确定性和对军事弹药的适用性

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摘要

Reliable, up-front information on physical and biological properties of emerging materials is essential before making a decision and investment to formulate, synthesize, scale-up, test, and manufacture a new material for use in both military and civilian applications. Multiple quantitative structure-activity relationships (QSARs) software tools are available for predicting a material's physical/chemical properties and environmental effects. Even though information on emerging materials is often limited, QSAR software output is treated without sufficient uncertainty analysis. We hypothesize that uncertainty and variability in material properties and uncertainty in model prediction can be too large to provide meaningful results. To test this hypothesis, we predicted octanol water partitioning coefficients (log P) for multiple, similar compounds with limited physical-chemical properties using six different commercial log P calculators (KOWWIN, MarvinSketch, ACD/Labs, ALogP, CLogP, SPARC). Analysis was done for materials with largely uncertain properties that were similar, based on molecular formula, to military compounds (RDX, BTTN, TNT) and Pharmaceuticals (Carbamazepine, Gemfibrizol). We have also compared QSAR modeling results for a well-studied pesticide and pesticide breakdown product (Atrazine, DDE). Our analysis shows variability due to structural variations of the emerging chemicals may be several orders of magnitude. The model uncertainty across six software packages was very high (10 orders of magnitude) for emerging materials while it was low for traditional chemicals (e.g. Atrazine). Thus the use of QSAR models for emerging materials screening requires extensive model validation and coupling QSAR output with available empirical data and other relevant information.
机译：在作出决定和投资来配制，合成，放大，测试和制造用于军事和民用领域的新材料之前，必须可靠，预先获得有关新兴材料物理和生物学特性的信息。多种定量构效关系（QSAR）软件工具可用于预测材料的物理/化学性质和环境影响。尽管有关新兴材料的信息通常很有限，但对QSAR软件的输出却没有进行足够的不确定性分析。我们假设材料特性的不确定性和可变性以及模型预测的不确定性可能太大而无法提供有意义的结果。为了检验该假设，我们使用六个不同的商用log P计算器（KOWWIN，MarvinSketch，ACD / Labs，ALogP，CLogP，SPARC）预测了物理化学性质有限的多种相似化合物的辛醇水分配系数（log P）。对具有很大不确定性的材料进行了分析，这些材料基于分子式与军用化合物（RDX，BTTN，TNT）和药品（卡马西平，吉非贝索）相似。我们还比较了经过深入研究的农药和农药分解产物（阿特拉津，DDE）的QSAR建模结果。我们的分析表明，由于新兴化学物质的结构变化而引起的可变性可能在几个数量级。对于新兴材料，六个软件包中的模型不确定性非常高（10个数量级），而对于传统化学品（例如阿特拉津）而言，模型的不确定性很小。因此，将QSAR模型用于新兴材料筛选需要大量的模型验证，并将QSAR输出与可用的经验数据和其他相关信息结合起来。

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  • 来源
    《Chemosphere》 |2009年第10期|1412-1418|共7页
  • 作者

    Erin R. Bennett; Jay Clausen; Eugene Linkov; Igor Linkov;

  • 作者单位

    Intertox, Inc., Seattle, WA, USA The Bioengineering Croup Inc., Salem, MA, USA;

    US Army Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, NH, USA;

    Carnegie Mellon University, Pittsburgh, PA, USA;

    Intertox, Inc., Seattle, WA, USA US Army Engineer Research and Development Center, Vicksburg, MS, USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    QSAR; EPI suite™; explosives; RDX; TNT;

    机译：QSAR;EPI套件™;炸药RDX;TNT;

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