HEPT类化合物的QSAR研究

为定量结构／活性相关性研究提取了量子化学参数,拓扑指数Am,分子连接性指数^mxt及疏水性常数,同时应用正交变换和最佳变量子集算法（Leaps-and-Bonds)进行了变量压缩和选择,进而实施了多元回归分析,并由此结果进行了HEPT类化合物（1－[(2-hydroxyethoxy)methyl]-6-(phenylthio)-thymine derivatives)的结构／活性关系的理论解释,进行了人工神经网络法对于该类化合物的活性预测,其结构明显好于多元回归法。     

酚类化合物在BSA和Aldrich-HA上吸附常数的QSAR研究

应用密度泛函方法(DFT)在B3LYP/6-311G**水平上全优化计算酚类化合物的量子化学参数,结合疏水性参数与酚类化合物在小牛血清蛋白BSA和腐殖质Aldrich-HA上的吸附常数进行定量构效关系研究(QSAR).经逐步回归筛选变量后,所建2个多元线性回归方程的相关系数R及去一法(LOO)交互检验复相关系数R2cv分别为0.955,0.882和0.943,0.819,用预测集样本进行了外部预测,所得外部预测集交互检验系数Q2ext分别为0.891和0.925.模型结果表明,ELUMO愈低,分子体积V愈大,化合物在BSA上吸附常数lgKDOC愈大;酚类化合物在腐殖质Aldrich-HA上不仅存在疏水作用吸附,还存在分子间作用力吸附,并以疏水作用吸附为主.     

色谱保留时间-结构参数定量关系研究

 利用量子化学和分子力学等方法计算了 48个烷基苯类化合物的部分电荷、偶极矩、分子表面积、分子体积等反映分子微观特征的结构参数 ,采用遗传函数算法 (GFA)进行回归 ,建立了拟合度高、物理意义明确、预测能力强的保留时间 -结构参数定量关系方程 ,找出了影响烷基苯类化合物色谱保留时间的主要结构因素。     

色谱灰色数模的建立及应用研究(Ⅳ)-CGM(1,1,N)模型的建立及烷基苯异构体保留行为的预测

应用灰色系统理论,推导出灰色N参数模型CGM(1,1,N),突破了现有单参数灰色模型的局限性.并用该模型讨论了色谱保留值与分子结构参数间的关系,对烷基苯的同分异构体色谱保留值及疏水性参数进行了预测.经实例验证,其预测精度较单参数灰色模型高,这为溶质保留行为的预测提供了一个实用方法,同时也拓宽了灰色模型的应用范围.     

氨基酸修饰环糊精对脂肪醇的分子识别研究

本文合成了 L-苯丙氨酸修饰 β-环糊精 (1 ) ,应用荧光光谱分光光度滴定方法测定了 2 5℃下 1与几种脂肪醇在磷酸缓冲溶液 (p H7.2 0 )中形成包结络合物的稳定常数 ,从主 -客体间的尺寸 /形状适合、客体分子的刚性及疏水性、疏水相互作用、范德华力等方面考察了主体对客体的分子识别 ,并与 L -色氨酸修饰β-环糊精的分子识别进行了比较。     

结合三维静电势参数研究二取代苯的定量结构－疏水性关系

对含有常见取代基的92个二取代苯化合物进行了结构优化和静电势及其导出参数的计算,运用多元线性回归方法对化合物的疏水常数与分子的结构参数进行了关联.结果表明,分子表面负静电势的加和ΣV－S、分子空间内最负的静电势Vmin、表面最大静电势Vs,max以及分子体积V、极性表面积APS和分子的偶极密度μ/V这六个参数,可以很好地用于表达这些化合物疏水性与分子结构间的定量关系,而不用具体考虑分子中极性基团间的相互作用.用建立起来的QSPR(quantitative structure-property relationship)关系式对111个类似化合物的疏水性进行了预测,获得了满意的结果.     

色谱灰色数模的建立及应用研究（Ⅳ）：CGM（1,1,N）模型的 …

应用灰色系统理论，推导出灰色Ｎ参数模型ＣＧＭ（１，１，Ｎ），突破了现有单参数灰色模型的局限性，并用该模型讨论了色谱保留值与分子结构参数间绎烷基苯的同分异构体色谱保留值及疏水性参数进行了预测，经实例验证，其预测精度较单参数灰色模型高，这为溶质保留行为的预测提供了一个实用方法，同时也拓宽了灰色模型的应用范围。     

利用分子路径指数矢量对烷烃摩尔响应值的估计与预测

 利用分子路径指数矢量表示烷烃分子结构方法 ,结合多元线性回归算法及反传神经网络算法 ,对烷烃摩尔响应值进行处理 ,获得了比文献更佳的预测效果 ,交互校验的相关系数达 0 96 8以上。     

超分子环糊精两亲分子

本文综述了“超分子环糊精两亲分子”的最新研究进展。超分子环糊精两亲分子主要包括疏水性修饰的环糊精衍生物(第一类)、环糊精衍生物与两亲分子的包合物(第二类)和环糊精衍生物与疏水性客体分子的包合物(第三类)。针对超分子环糊精两亲分子及其自组装体系的研究不但丰富了由诺贝尔化学奖得主Lehn等所提出的超分子化学的内涵,实现了多学科的交叉,而且在生物模拟、智能材料以及可控的、具有疗效的药物输运与缓释等领域具有潜在的应用前景。     

羟基取代的烷基苯磺酸盐在水/气和水/癸烷界面的结构特点（摘要）

驱油表面活性剂的分子设计是一项重要的研究课题.设计新型高效的驱油表面活性剂关键的问题在于如何洞察表面活性剂的结构和功能的关系.长线性烷基苯磺酸盐是一类非常流行的表面活性剂,广泛应用于工业和日常生活中.关于烷基苯磺酸盐的结构和功能研究已有大量的实验和理论工作报道.近来,结合分子设计的思想,实验上合成了新型的羟基取代的烷基苯磺酸盐表面活性剂,并研究了这类新型表面活性剂动态的界面行为.我们从理论上利用分子动力学模拟的方法研究了羟基取代的烷基苯磺酸盐单分子层在水/气和水/癸烷界面的结构特点.从液体密度剖面图、氢键、表面活性剂聚集结构和有序参数等方面,详细报道了2-羟基-3-癸基-5-辛基苯磺酸钠这种新型阴离子表面活性剂的界面特征.模拟结果表明随着表面活性剂分子数目的增加,每个表面活性剂在单分子层上形成分子内氢键的平均数目将下降,但形成分子内氢键的结构仍处于主导地位;烷基尾链的疏水部分,尤其是苯环3号位上取代的癸基随着表面活性剂覆盖度增大,向界面外延伸并且更加有序;二维径向分布函数描绘了表面活性剂聚集结构的特点并暗示了癸烷相将影响表面活性剂疏水部分的取向;表面活性剂分子容易形成长程氢键结构.我们的模拟结果是对实验研究的一个重要补充.此外,模拟中我们利用gromacs和ffamber程序,使用了全原子模型,这将为模拟烷基苯磺酸盐表面活性剂的界面行为提供新的方案.     

An Extreme Learning Machine Algorithm to Predict the In-flight Particle Characteristics of an Atmospheric Plasma Spray Process

A robust single hidden layer feed forward neural network (SLFN) is used in this study to model the in-flight particle characteristics of the atmospheric plasma spray (APS) process with regard to the input processing parameters. The in-flight particle characteristics influence the structure and properties of the APS coating and, thus, are considered important parameters to comprehend the manufacturing process. The training times of traditional back propagation algorithms, mostly used to model such processes, are far slower than desired for implementation of an on-line control system. Use of slow gradient based learning methods and iterative tuning of all network parameters during the learning process are the two major causes for such slower learning speed. An extreme learning machine (ELM) algorithm, which randomly selects the input weights and biases and analytically determines the output weights, is used in this work to train the SLFNs. Performance comparisons of the networks trained with ELM algorithm and standard error back propagation algorithms are presented. It is found that networks trained with ELM have good generalization performance, much shorter training times and stable performance with regard to the changes in number of hidden layer neurons. The trends represent robustness of the trained networks and enhance reliability of the application of the artificial neural network in modelling APS processes.     

Identification of rhubarbs by using NIR spectrometry and temperature-constrained cascade correlation networks

Temperature-constrained cascade correlation networks (TCCCNs) were used to identify powdered rhubarbs based on their near-infrared spectra. Different network configurations that used multiple network models with single output (Uni-TCCCN) and single networks with multiple outputs (Multi-TCCCN) were compared. Comparative studies were made by using Latin-partitions and leave-one-out cross-validation methods. Results showed that multiple networks with single output predicted generally better than single network with multiple outputs. Better results with TCCCN models were obtained compared with conventional back propagation neural networks (BPNNs). The effects of parameters on correct identification and parameter optimizations were discussed in detail. With optimized neural network training parameters, NIR spectra from powdered rhubarb samples were classified by a TCCCN model with 100% accuracy.     

Prediction of phase equilibrium properties for complicated macromolecular systems by HGALM neural networks

Traditional error back propagation is a widely used training algorithm for feed forward neural networks (FFNNs). However, it generally encounters two problems of slow learning rate and relative low accuracy. In this work, a hybrid genetic algorithm combined with modified Levenberg–Marquardt algorithm (HGALM) was proposed for training FFNNs to improve the accuracy and decrease the time depletion comparing to the traditional EBP algorithm. The FFNNs based on HGALM were used to predict the binodal curve of water–DMAc–PSf system and protein solubility in lysozyme–NaCl–H₂O system. The results would be used for guiding experimental researches in preparation of asymmetry polymer membrane and optimization of protein crystal process.     

神经网络方法在血管紧张素转换酶抑制剂定量构效关系建模中的应用

对20个ACEI化合物用量子化学方法进行结构优化并计算出10个参数,用9种不同隐含层节点数的BP神经网络研究了ACEI的定量构效关系,建立了节点为10/6/1的三层BP神经网络模型。结果表明:以量化理论计算所得参数可以构建合理的ACEI定量构效关系模型,神经网络模型M6的r2=0.995,S=0.050,6个验证集化合物的残差平方和为0.002,预测能力明显强于多元线形回归模型,亦优于同类文献报道,可作为ACEI研发领域中预测先导化合物活性的理论工具。     

神经网络法在使用裂解气相色谱鉴别中草药中的应用

将以误差反向传播为训练算法的前馈式人工神经网络（ＢＰ－ＡＮＮ）首次艇于中草药的裂解气相色谱谱图解析。重点考察了如何表征和提取复杂的裂解色谱图中有价值信息,用于主成分分析方法处理后输入到有数经优化的神经网络中。实验证明,该广阔示仅可以正确识别样品所属种类,耐用对一示同实验时间、数据残缺等原因造成的噪音具有优异的抗干扰能力。     

Artificial neural networks in analysis of indinavir and its degradation products retention

Artificial neural networks (ANN) are biologically inspired computer programs designed to simulate the way in which the human brain processes the information. In the past few years, coupling of experimental design (ED) and ANN became useful tool in the method optimization. This paper presents the application of ED-ANN in analysis of chromatographic behavior of indinavir and its degradation products. According to preliminary study, full factorial design 2⁴ was chosen to set input variables for network training. Experimental data (inputs) and results for retention factors from experiments (outputs) were used to train the ANN with aim to define correlation among variables. For networks training multi-layer perceptron (MLP) with back propagation (BP) algorithm was used. Network with the lowest root mean square (RMS) had 4-8-3 topology. Predicted data were in good agreement with experimental data (correlation was higher than 0.9713 for training set). Regression statistics confirmed good ability of trained network to predict compounds retention.     

神经网络用于色谱研究(Ⅰ)─GC保留值估算

神经网络用于色谱研究（Ⅰ）──ＧＣ保留值估算李志良,八重治,梁本熹,石乐明（湖南大学化学化工系,长沙,４１００８２;日本国立丰桥技术科学大学,中国科学院化工冶金研究所）关键词神经网络,修饰反向传播算法,气相色谱保留值神经网络（ＮＮ）近年来获得突破性进...     

Predicting the standard enthalpy (deltaH0f) and entropy (S0) of alkanes by artificial neural networks

Artificial Neural Networks (ANNs) with Extended Delta-Bar-Delta (EDBD) back propagation learning algorithm have been developed to predict the standard enthalpy and entropy of 87 acyclic alkanes. Molecular weight, boiling point and density of the compounds were used as input parameters. The network's architecture and parameters were optimized to give maximum performances. The best network was a 3-6-2 ANN, and the optimum learning epoch was about 1320. The results show that the maximum relative errors of enthalpy and entropy are less than 3%. They reveal that the performances of ANNs for predicting the enthalpy and entropy of alkanes are satisfying.     

Classification of Tieguanyin Tea with an Electronic Tongue and Pattern Recognition

Analysis of four Tieguanyin teas from different origins were performed using an electronic tongue, which has significant advantages in terms of accuracy and precision for pattern recognition. Hierarchical cluster analysis and principal component analysis were then applied to identify origins of these teas, and a distinct separation was observed. The back propagation neural network (BPNN) and the back propagation neural network with the Levenberg-Marquardt training algorithm (LMBP) were applied to build identification models. The Levenberg-Marquardt training algorithm model outperformed the back propagation neural network, as the identification performances of the former model were 100% in the training and prediction sets when four principal components were used. The results demonstrate that an electronic tongue with pattern recognition is suitable to classify Tieguanyin tea and shows broad potential in food inspection and quality control.     

二进小波神经网络用于示波计时电位测定

提出了二进小波神经网络的结构及算法，并用于单组分和多组分示波计时电位信号的浓度计算。在二进小波神经网络中选用了Morlet母小波和修理的误差反传前向神经网络。探讨了二进小波神经网络中的中小波基个数，初始学习速率因子和动量因子等参数对网络预测结果的影响。结果表明：二进小波神经网络对双组分和单组分示波计时电位信号中去极剂浓度的预测均有很好效果。