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首页> 美国卫生研究院文献>International Journal of Molecular Sciences >Principal Component Analysis Coupled with Artificial Neural Networks—A Combined Technique Classifying Small Molecular Structures Using a Concatenated Spectral Database
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Principal Component Analysis Coupled with Artificial Neural Networks—A Combined Technique Classifying Small Molecular Structures Using a Concatenated Spectral Database

机译：主成分分析与人工神经网络结合的联合技术使用级联光谱数据库对小分子结构进行分类

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In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN) and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen), or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry) and GC-MS (gas chromatography-mass spectrometry) spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA). The scores of the forensic compounds on the main principal components (PCs) were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network) with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%), as well as a good selectivity (a rate of true negatives TN = 92.77%). A comparative analysis of the validation results of all expert systems is presented, and the input variables with the highest discrimination power are discussed.
机译：在本文中，我们基于预处理的光谱数据库，介绍了一些预测建模化合物的类身份的专家系统。专家系统是使用人工神经网络（ANN）构建的，旨在预测未知化合物是否具有苯丙胺（兴奋剂和迷幻剂）的毒理活性，或者是否为非苯丙胺。为了绕开管制滥用毒品的法律，黑市上经常引入新的化学结构。它们是通过在禁止分子的各个位置上添加或改变取代基来稍微修饰受控分子结构而获得的。结果，即使没有特别列出，如今也不能使用与构成禁用类别的物质相似的物质。因此，对于流行病学，临床和法医目的，非常需要能够在分子水平上进行建模然后鉴定相似性的可靠，快速且可访问的系统。为了获得专家系统，我们已经预处理了一个连接的光谱数据库，代表了103种法医化合物的GC-FTIR（气相色谱-傅立叶变换红外光谱）和GC-MS（气相色谱-质谱）光谱。该数据库用作主成分分析（PCA）的输入。然后将主要主要成分（PC）上的取证化合物得分作为ANN系统的输入。我们已经构建了八个具有不同输入变量数量的PC-ANN系统（主成分分析与人工神经网络结合）：15台PC，16台PC，17台PC，18台PC，19台PC，20台PC，21台PC和22台PC。发现最好的专家系统是构建有18台PC的ANN网络，其解释的差异为77％。该专家系统具有最佳的灵敏度（分类率为C = 100％，真实阳性率TP = 100％）以及良好的选择性（真实阴性率TN = 92.77％）。对所有专家系统的验证结果进行了比较分析，并讨论了具有最高判别力的输入变量。

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