硝基苯类化合物的FMO位电荷密度能S_(Ei)~(HO)与生物毒性的QSAR研究

应用简易的量化方法计算了20多种硝基苯衍生物中的64个芳环FMO位电荷密度 能S_(Ei)~(HO)，用回归法建立了一个新的生物毒性评价方程，-lg LC_(50)=0. 6191log K_(ow)+0.1881S_(Ei)~(HO)+4.0894，应用所得方程，预测有机物的生物 毒性，方程对大多数化合物拟和很好。结果表明，所研究的有机物生物毒性同S_ (Ei)~(HO)和log K_(ow)密切相关，同化合物与酶的活性点复合或反应是生物中毒 的主要因素。     

QSAR结合人工神经网络预测取代氯苯酚生物毒性

取代氯苯酚类化合物是有机化学工业中必不可少的重要原料,现已有邻氯苯酚等4种酚类化合物被美国环保局定为优先控制的有机污染物[1].对此,国内外有关专家对取代氯苯酚的生物毒性进行了广泛研究,但都没有得到有效的构效关系[2-3 ].因此,通过建立取代氯苯酚生物毒性与分子结构间的定量关系,对于预测其生物毒性有着实际的意义.     

人工神经网络用于对位取代苯酚定量构效关系的研究

本文利用误差反向传播(BP)的人工神经网络(ANN)模型研究了对位取代苯酚衍生物的生物活性与其结构及物理化学性质参数之间的定量构效关系。优化了ANN模型的参数设计,提出了动态调节网络学习速率的经验规则以改善网络的性能。采用f(x)=1/(1+e~(-x))作为网络节点的输入输出转换函数的三层神经网络具有较佳性能,当取隐含节点数为10时,该网络预测26个对位取代苯酚衍生物生物活性的均方误差(mse)为0.036,优于常规构效关系预测结果。     

氯苯胺类化合物对发光细菌毒性的定量构效关系

应用密度泛函理论的BLYP方法,对14种氯苯胺类化合物进行几何构型优化,得到稳定构型下的量子化学参数,研究了该类化合物对发光细菌毒性作用的定量结构-活性关系(QSAR),应用逐步回归方法建立了相关方程.结果表明:该类化合物对发光细菌的毒性作用随分子最低空轨道(LUMO)能级的降低而增大,随分子中氯取代数目的增加而增大.     

脂肪醇的生物毒性与结构参数的相关性研究

应用量子化学从头算HF/3-21G方法得到了14种脂肪醇分子的优势构象,利用HF/3-21G法和分子图形学技术获得其电子结构、几何结构参数和连接性指数,并将这些参数与脂肪醇对番茄和红蜘蛛的毒性相关联.结果表明,脂肪醇对番茄的生物毒性与一阶分子连接性指数1X和羟基电荷QOH之间存在良好的二元线性相关性,而脂肪醇对红蜘蛛的生物毒性与一阶分子连接性指数1X和分子最高已占轨道能EH之间存在良好的二元线性相关性,成功地建立脂肪醇对番茄和红蜘蛛毒性的构效关系式.     

硝基芳烃对黑呆头鱼毒性定量构效关系的研究

用CNDO/2法计算50种硝基芳烃化合物的净电荷(Q_C、Q_N及Q_-NO2);使用MNDO法计算其中42种化合物的E_LUMO、E_HOMO、生成热之差△(△H_f)及偶极矩μ。定量分析了7种量化参数与黑呆头鱼毒性96h-LC₅₀的构效关系,通过统计分析,得到如下模式:式中:-1gLC₅₀=11. 35-1. 28E_LUMO-9.17Q_N+0. 46E_HOMO-0.12μ n=35,r=0.920,s=0.298。应用所得方程及量化参数讨论所研究系列化合物在鱼体内的毒性作用。     

N—亚硝基化合物的结构／致癌活性的三维构效关系研究

用比较力场分析研究了Ｎ－亚硝基化合物的结构与致癌活性的关系，考察了网络结构和探针原子对结果的影响。结果表明，立体效应和静电作用场是描述其致癌活性和进行结构性能关系研究的最重要的结构参数。     

投影边缘在硝基苯类化合物构效关系研究中的应用

在三维投影的基础上,对投影的边缘进行了描述,并以其进行了定量结构活性相关研究.实验结果表明,与Am指数、指示变量和量化参数相结合,可使其数学模型得到明显提高.通过人工神经网络对硝基苯类化合物的毒性进行预测,结果令人满意.     

含氟新农药的构效关系研究Ⅰ.经典QSAR分析

为了能更深入地认识含氟新化合物作为农药的生物活性和其结构间的关系,建立有意义的构效关系模型,我们用经典QSAR(定量构效关系)方法研究了三十三个含氟化合物的两种不同的生物活性与结构的关系,其对抗黄瓜疫病活性模型有很好的解释能力和预测能力,并根据这个模型设计了一些新的活性结构.而对抑制西瓜白绢病的活性数据的处理未能获得理想模型.通过这一工作确立了先应用聚类等定性分析方法,再用多元统计分析方法作更深入研究的QSAR研究模式.     

Predicting nucleic acid torsion angle values using artificial neural networks

By means of an error back-propagation artificial neural network, a new method to predict the torsion angles , and from torsion angles , , and for nucleic acid dinucleotides is introduced. To build a model, training sets and test sets of 163 and 81 dinucleotides, respectively, with known crystal structures, were assembled. With 7 hidden units in a three-layered network a model with good predictive ability is constructed. About 70 to 80% of the residuals for predicted torsion angles are smaller than 10 degrees. This means that such a model can be used to construct trial structures for conformational analysis that can be refined further. Moreover, when reasonable estimates for , , and are extracted from COSY experiments, this procedure can easily be extended to predict torsion angles for structures in solution.     

Forecasting human exposure to PM10 at the national level using an artificial neural network approach

A neural network model for predicting country‐level concentrations of the fraction of particulates in the air with sizes less than 10 µm (PM10) has been developed using widely available sustainability and economical/industrial parameters as inputs. The model was trained and validated with the data for 23 European Union (EU) countries plus the EU27 as a group for the period from 2000 to 2008. The inputs for the model were selected using correlation analyses. Country‐level PM10 concentration data that were used as a model output were obtained from the World Bank. The artificial neural network (ANN) model, created with inputs chosen by correlation analyses, has shown very good performance in the forecast of country‐level PM10 concentrations. The mean absolute error for the ANN model prediction, in the case of most of the EU countries, was less than 13%, indicating stable and accurate predictions. The predictions obtained from the principal component regression model, which was trained and tested using the same datasets and input variables, had mean absolute errors from 20% to 150% for most of the countries. The wide availability of input parameters used in this model can overcome the problem of lack and scarcity of data in many countries, which can in turn prevent the determination of human exposure to PM10 at the national level. Copyright © 2013 John Wiley & Sons, Ltd.     

Optimization of extraction of linarin from Flos chrysanthemi indici by response surface methodology and artificial neural network

The extraction of linarin from Flos chrysanthemi indici by ethanol was investigated. Two modeling techniques, response surface methodology and artificial neural network, were adopted to optimize the process parameters, such as, ethanol concentration, extraction period, extraction frequency, and solvent to material ratio. We showed that both methods provided good predictions, but artificial neural network provided a better and more accurate result. The optimum process parameters include, ethanol concentration of 74%, extraction period of 2 h, extraction three times, solvent to material ratio of 12 mL/g. The experiment yield of linarin was 90.5% that deviated less than 1.6% from that obtained by predicted result.     

Artificial neural network‐based drug design for diabetes mellitus using flavonoids

Diabetes mellitus is a chronic metabolic disease involving the failure to regulate glucose blood levels in the body and has been linked with numerous detrimental complications. Studies have shown that these complications can be linked to the activities of aldose reductase (AR), an enzyme of the polyol pathway. Flavonoids have been identified as good AR inhibitors (ARIs) and are also strong antioxidants with radical scavenging (RS) activity. As such, flavonoids show potential to become a better class of ARIs because they are able to concurrently address the oxidative stress issue. In this article, we carried out quantitative structure‐activity relationship analysis of flavones and flavonols (members of flavonoid family) using artificial neural networks. Three computer experiments were conducted to study the influence of hydrogen (H), hydroxyl (? OH), and methoxyl (? CH₃) functional groups on eight substitution sites of the lead flavone molecule and to predict potential ARIs. Of 6561 possible flavones and flavonols, in experiment 1, we predicted 69 potent ARIs, and in experiment 2, we predicted 346 compounds with strong RS activity. In experiment 3, we combined these results to find overlapping compounds with both strong AR inhibition and RS activity and we are able to predict 10 potent compounds with strong AR inhibition (IC₅₀ < 0.3 μM) and RS activity (IC₂₅ < 1.0 μM). These 10 compounds show promise of being good therapeutic agents in the prevention of diabetic complications and is suggested to undergo further wet bench experimentation to prove their potency. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2011     

Experimental study on the 3D‐printed plastic parts and predicting the mechanical properties using artificial neural networks

This study investigates the mechanical properties of 3D‐printed plastic parts fabricated using Fused Deposition Modeling (FDM). For this purpose, a 3D printer named KASAME was designed and built by the researchers. The test samples were fabricated using polylactic acid (PLA). The experiments were conducted using three melt temperatures (190°C, 205°C, and 220°C), four layer thickness values (0.06 mm, 0.10 mm, 0.19 mm, and 0.35 mm), and three raster pattern orientations (+45°/?45° [the crisscross pattern], horizontal and vertical). Tensile strength tests were performed to determine tensile strength values of the samples and fracture surfaces were also analyzed. Using artificial neural networks, a mathematical model for the tensile test results was generated corresponding to the raster pattern employed in 3D fabrication. Tensile strength tests indicated that melt temperature, layer thickness, and raster pattern orientation had a significant effect on the tensile strengths of the samples. According to the result of the experiment, the maximum average tensile strength values were observed for the samples fabricated using the crisscross raster pattern. The analysis of variance (ANOVA) table shows the raster pattern (PCR) value of 48.68% was obtained with the highest degree of influence. With respect to R ², the best performing artificial neural network model, with test and training values of 0.999199 and 0.999997, respectively, was observed to be the crisscross raster pattern. Copyright © 2016 John Wiley & Sons, Ltd.     

A polarizable force field for water using an artificial neural network

A force field for liquid water including polarization effects has been constructed using an artificial neural network (ANN). It is essential to include a many-body polarization effect explicitly into a potential energy function in order to treat liquid water which is dense and highly polar. The new potential energy function is a combination of empirical and nonempirical potentials. The TIP4P model was used for the empirical part of the potential. For the nonempirical part, an ANN with a back-propagation of error algorithm (BPNN) was introduced to reproduce the complicated many-body interaction energy surface from ab initio quantum mechanical calculations. BPNN, described in terms of a matrix, provides enough flexibility to describe the complex potential energy surface (PES). The structural and thermodynamic properties, calculated by isobaric-isothermal (constant-NPT) Monte Carlo simulations with the new polarizable force field for water, are compatible with experimental results. Thus, the simulation establishes the validity of using our estimated PES with a polarization effect for accurate predictions of liquid state properties. Applications of this approach are simple and systematic so that it can easily be applied to the development of other force fields besides the water-water system.     

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.     

应用人工神经网络预测多氯代二苯骈呋喃的色谱保留指数

以量子化学方法在密度泛函B3LYP/6-31G(d)水平上计算得到了多氯代二苯骈呋喃系列化合物(PCDF)分子的结构参数:最高占据轨道能(EHOMO)、最低空轨道能(ELUMO)、最正原子净电荷(q+)、最负原子净电荷(q-)、分子偶极矩(μ)、极化率(α)、分子平均体积(Vm)、恒容热容(C■V).采用误差反向传播(BP)算法的人工神经网络,建立了EHOMO、ELUMO、q+、q-、μ、α、Vm、C■V与PCDF色谱保留指数之间关系的模型,检测样本的预报值与实验值相对误差范围为-1.66%².39%,平均相对误差为0.31%,达到了很好的预测效果.     

Citrate and calcium determination in flavored vodkas using artificial neural networks

The development of multianalyte sensing schemes by combining indicator-displacement assays with artificial neural network analysis (ANN) for the evaluation of calcium and citrate concentrations in flavored vodkas is presented. This work follows a previous report where an array-less approach was used for the analysis of unknown solutions containing the structurally similar analytes, tartrate and malate. Herein, a two component sensor suite consisting of a synthetic host and the commercially available complexometric dye, xylenol orange, was created. Differential UV-Visible spectral responses result for solutions containing various concentrations of calcium and citrate. The quantitation of the relative calcium and citrate concentrations in unknown mixtures of flavored vodka samples was determined through ANN analysis. The calcium and citrate concentrations in the flavored vodka samples provided by the sensor suite and the ANN methodology described here are compared to values reported by NMR of the same flavored vodkas. We expect that this multianalyte sensing scheme may have potential applications for the analysis of other complex fluids.