Assessment of applicability domain for multivariate counter-propagation artificial neural network predictive models by minimum Euclidean distance space analysis: A case study

Alongside the validation, the concept of applicability domain (AD) is probably one of the most important aspects which determine the quality as well as reliability of the established quantitative structure–activity relationship (QSAR) models. To date, a variety of approaches for AD estimation have been devised which can be applied to particular type of QSAR models and their practical utilization is extensively elaborated in the literature. The present study introduces a novel, simple, and effective distance-based method for estimation of the AD in case of developed and validated predictive counter-propagation artificial neural network (CP ANN) models through a proficient exploitation of the Euclidean distance (ED) metric in the structure-representation vector space. The performance of the method was evaluated and explained in a case study by using a pre-built and validated CP ANN model for prediction of the transport activity of the transmembrane protein bilitranslocase for a diverse set of compounds. The method was tested on two more datasets in order to confirm its performance for evaluation of the applicability domain in CP ANN models. The chemical compounds determined as potential outliers, i.e., outside of the CP ANN model AD, were confirmed in a comparative AD assessment by using the leverage approach. Moreover, the method offers a graphical depiction of the AD for fast and simple determination of the extreme points.     

Comparison of criteria used to access carcinogenicity in CPANN QSAR models versus the knowledge-based expert system Toxtree

The primary goal of this study was to describe and compare the criteria used to assess carcinogenic activity. The statistically-based predictive quantitative structure–activity relationship (QSAR) models based on the counter propagation artificial neural network (CPANN) algorithm, and knowledge-based expert systems based on a decision tree structural alert (SA) approach (Toxtree application), were considered. The integration of the QSAR (CPANN models) and SAR (Toxtree SA application) approach contributed to the mechanistic understanding of the QSAR model considered. The mapping technique inherent to CPANN Kohonen enables us to relate the similarities or dissimilarities within a congeneric set of chemicals with particular SAs for carcinogenicity. The focus of our investigations was the similarities and dissimilarities of the features used in the QSAR and SAR methods. Due to the complexity of the carcinogenic endpoint, the integration of different approaches allows the models to be improved and provides a valuable technique for evaluating the safety of chemicals.     

含氧化合物气相色谱保留指数的预测

通过对部分含氧化合物(醇、酯、醛、酮)在不同固定相不同柱温下的849个样本的气相色谱保留指数值(RI)与其部分参数:拓扑指数(mQ)、定位基参数(Sox)、固定液极性值(CP)及柱温(T)建立定量结构-色谱保留相关(QSRR)模型。分别利用多元线性回归(MLR)、偏最小二乘回归(PLSR)、人工神经网络(ANN)建模,同时采用内部及外部双重验证的办法对所得模型稳定性能进行深入分析和检验,建模计算值、留一法(LOO)交互检验(CV)预测值和外部样本预测值的复相关系数Rcum、QLOO和Rext分别为0.9832、0.9829和0.9836(MLR);0.9832、0.9830和0.9836(PLSR);0.9910、0.9909和0.9900(ANN)。结果表明:所建定量结构保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了含氧化合物(醇、酯、醛、酮)在不同色谱条件下气相色谱保留指数的变化规律。     

Prediction of Chromatographic Retention of Pyrazine and Alkylpyrazines in RP-LC

Retention prediction models for a group of pyrazines chromatographed under reversed-phase mode were developed using multiple linear regression (MLR) and artificial neural networks (ANNs). Using MLR, the retention of the analytes were satisfactorily described by a two-predictor model based on the logarithm of the partition coefficient of the analytes (log P) and the percentage of the organic modifier in the mobile phase (ACN or MeOH). ANN prediction models were also derived using the predictors derived from MLR as inputs and log k as outputs. The best network architecture was found to be 2-2-1 for both ACN and MeOH data sets. The optimized ANNs showed better predictive properties than the MLR models especially for the ACN data set. In the case of the MeOH data set, the MLR and ANN models have comparable predictive performance.     

Classification of wine samples by means of artificial neural networks and discrimination analytical methods

 The three-layer artificial neural network (ANN) model with back-propagation (BP) of error was used to classify wine samples in six different regions based on the measurements of trace amounts of B, V, Mn, Zn, Fe, Al, Cu, Sr, Ba, Rb, Na, P, Ca, Mg,  K using an inductively coupled plasma optical emission spectrometer (ICP-OES). The ANN architecture and parameters were optimized. The results obtained with ANN were compared with those obtained by cluster analysis, principal component analysis, the Bayes discrimination method and the Fisher discrimination method. A satisfactory prediction result (100%) by an artificial neural network using the jackknife leave-one-out procedure was obtained for the classification of wine samples containing six categories. Received: 12 July 1996/Revised: 9 October 1996/Accepted: 12 October 1996     

烯烃气相色谱保留指数的QSRR研究

通过对184个烯烃类化合物在不同固定相不同柱温下的617个样本的气相色谱保留指数值(RI)与其部分参数:拓扑指数(mQ)、偶极矩(DPL)、固定液极性值(CP)及柱温(T)建立定量-色谱保留相关(QSRR)模型.分别利用多元线性回归(MLR)、偏最小二乘回归(PLSR)、人工神经网络(ANN)建模,同时采用内部及外部双重验证的办法对所得模型稳定性能进行深入分析和检验,建模计算值、留一法(LOO)交互检验(CV)预测值和外部样本的复相关系数Rcum,QLOO和Rext分别为0.999 2,0.998 4和0.999 2(MLR);0.999 0,0.998 0和0.999 1(PLSR);0.999 4,0.998 7和0.999 2(ANN).结果表明:所建定量结构保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了烯烃类化合物在不同固定相不同柱温上气相色谱保留指数的变化规律.     

Simultaneous dual wavelength spectrophotometric determination of citric and ascorbic acids using an artificial neural network

In this study, a very simple spectrophotometric method for the simultaneous determination of citric and ascorbic acid based on the reaction of these acids with a copper(II)-ammonia complex is presented. The Cu²⁺-NH₃ complex (with λ_max = 600 nm) was decomposed by citrate ion and formed a Cu²⁺-citrate complex (with λ_max = 740 nm). On the other hand, during the reaction of ascorbic acid with copper(II)-ammonia complex, ascorbic acid is oxidized and the copper(II)-ammonia complex is reduced to the copper(I)-ammonia complex and the absorbance decreases to 600 nm. Although there is a spectral overlap between the absorbance spectra of complexes Cu²⁺-NH₃ and Cu²⁺-citrate, they have been simultaneously determined using an artificial neural network (ANN). The absorbances at 600 and 740 nm were used as the input layer. The ANN architectures were different for citric and ascorbic acid. The output of the citric acid ANN architecture was used as an input node for the ascorbic acid ANN architecture. This modification improves the capability of the ascorbic acid ANN model for the prediction of ascorbic acid concentrations. The dynamic ranges for citric and ascorbic acid were 1.0–125.0 and 1.0–35.0 mM, respectively. Finally, the proposed method was successfully applied to the determination of citric and ascorbic acids in vitamin C tablets and some powdered drink mixes. The text was submitted by the authors in English.     

Conjugation of proteins with chelating polymers via water-soluble carbodiimide andN-hydroxysulfosuccinimide

Water-soluble chelating polymers (CP) based on polylysine and diethylenetriaminepentaacetic acid (DTPA) have been prepared. The effect of the number of DTPA-groups in the polymer chain on the process of CP carbodiimide-mediated coupling to proteins has been studied. CP obtained were conjugated with proteins via carbodiimide andN-hydroxysulfosuccinimide (HSSI). The optimal conditions of CP activation were determined using model low-molecular-weight amine. It was shown that the addition of HSSI to an activation mixture increases the coupling efficiency of CP with immunoglobulins by 3‐4-fold compared with carbodiimide alone. Possible mechanisms of this phenomenon are discussed.     

Artificial Neural Network (ANN) design for Hg–Se interactions and their effect on reduction of Hg uptake by radish plant

The tendency of selenium to interact with heavy metals in presence of naturally occurring species has been exploited for the development of green bioremediation of toxic metals from soil using Artificial Neural Network (ANN) modeling. The cross validation of the data for the reduction in uptake of Hg(II) ions in the plant R. sativus grown in soil and sand culture in presence of selenium has been used for ANN modeling. ANN model based on the combination of back propagation and principal component analysis was able to predict the reduction in Hg uptake with a sigmoid axon transfer function. The data of fifty laboratory experimental sets were used for structuring single layer ANN model. Series of experiments resulted into the performance evaluation based on considering 20% data for testing and 20% data for cross validation at 1,500 Epoch with 0.70 momentums The Levenberg–Marquardt algorithm (LMA) was found as the best of BP algorithms with a minimum mean squared error at the eighth place of the decimal for training (MSE) and cross validation.