Roncaglioni Alessandra, Novic Marjana, Vracko Marjan, Benfenati Emilio
Laboratory of Chemometrics, National Institute of Chemistry, Hajdrihova 19, SLO-1000 Ljubljana, Slovenia.
J Chem Inf Comput Sci. 2004 Mar-Apr;44(2):300-9. doi: 10.1021/ci030421a.
A methodology for the classification of endocrine disruption chemicals is proposed. It is based on a data set of 106 substances extracted from the list of 553 chemicals that were inspected by the European Union Commission for the scientific evidence of their endocrine disruption activity. The substances belong to different categories defined in the EU Commission report: (i) literature evidence for certainly active as endocrine disrupters, (ii) for potentially active, (iii) for less probable active--lacking evidence, and (iv) for certainty nonactive. 3D molecular coordinates were calculated using the AM1or the PM3 optimization method. From 3D coordinates an extensive set of molecular descriptors was calculated. The classification model based on the counterpropagation neural network was constructed and evaluated. This is the first time that the counterpropagation neural network is applied for the classification of compounds regarding their literature evidence for the endocrine disruption activity. The developed classification model is proposed as a tool for a preliminary assessment of potential endocrine disrupters, which would help the assessors to make the priority list for a large amount of chemicals that have to be tested with more expensive in vitro and in vivo methods.
提出了一种内分泌干扰化学物质的分类方法。它基于从欧盟委员会检查的553种化学物质清单中提取的106种物质的数据集，这些化学物质需有内分泌干扰活性的科学证据。这些物质属于欧盟委员会报告中定义的不同类别：（i）肯定具有内分泌干扰活性的文献证据，（ii）潜在活性的，（iii）活性可能性较小——缺乏证据的，以及（iv）肯定无活性的。使用AM1或PM3优化方法计算三维分子坐标。从三维坐标计算出大量的分子描述符。构建并评估了基于反向传播神经网络的分类模型。这是首次将反向传播神经网络应用于根据化合物的内分泌干扰活性文献证据对其进行分类。所开发的分类模型被提议作为一种初步评估潜在内分泌干扰物的工具，这将有助于评估人员为大量必须用更昂贵的体外和体内方法进行测试的化学物质制定优先级列表。
