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毒性定量构效关系分析中价层的相关权重

Correlation weighting of valence shells in QSAR analysis of toxicity.

作者信息

Toropov Andrey A, Benfenati Emilio

机构信息

Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche 'Mario Negri', Via Eritrea 62, 20157 Milan, Italy.

出版信息

Bioorg Med Chem. 2006 Jun 1;14(11):3923-8. doi: 10.1016/j.bmc.2006.01.038. Epub 2006 Feb 3.

DOI:10.1016/j.bmc.2006.01.038
PMID:16460943
Abstract

In the rainbow trout (Oncorhynchus mykiss), we studied the acute toxicity LC(50)-96 h of 274 organic pesticides with a wide variety of molecular structures. Optimization of correlation weights of local and global graph invariants (OCWLGI) gave quantitative structure-activity relationships (QSARs) for predicting toxicity. We used a labeled hydrogen-filled graph (LHFG) to elucidate the molecular structure. We also used the extended connectivity of zero ((0)EC(k)), first ((1)EC(k)), and second ((2)EC(k)) order, numbers of path lengths 2 (P2(k)) and 3 (P3(k)) starting from a given vertex in the LHFG, and valence shells of second order (S2(k)). S2(k) is the sum of the degree of vertices at distance 2 from a given vertex k. The presence of three-, five-, and six-member cycles and hydrogen bond indices suggested they might be used as global LHFG invariants. We applied this method to a broad set of pesticides, to predict toxicity for the trout. The best model used weighted S2(k) and global LHFG invariants. Statistical characteristics of this model are as follows: n=233, r(2)=0.7689, r(2)(pred)=0.7688, s=0.75, F=769 (training set); n=41, r(2)=0.6421, r(2)(pred)=0.4241, s=1.14, F=70 (test set).

摘要

在虹鳟鱼(Oncorhynchus mykiss)中,我们研究了274种具有多种分子结构的有机农药的96小时急性毒性半数致死浓度(LC(50))。局部和全局图不变量相关权重的优化(OCWLGI)给出了用于预测毒性的定量构效关系(QSARs)。我们使用带标记氢填充图(LHFG)来阐明分子结构。我们还使用了零阶((0)EC(k))、一阶((1)EC(k))和二阶((2)EC(k))的扩展连接性、从LHFG中给定顶点出发的路径长度为2(P2(k))和3(P3(k))的数量,以及二阶价层(S2(k))。S2(k)是与给定顶点k距离为2的顶点度数之和。三元、五元及六元环的存在和氢键指数表明它们可能被用作全局LHFG不变量。我们将此方法应用于广泛的农药组,以预测对虹鳟鱼的毒性。最佳模型使用了加权S2(k)和全局LHFG不变量。该模型的统计特征如下:n = 233,r(2) = 0.7689,r(2)(pred) = 0.7688,s = 0.75,F = 769(训练集);n = 41,r(2) = 0.6421,r(2)(pred) = 0.4241,s = 1.14,F = 70(测试集)。

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