用神经网络法鉴别十二碳共轭双烯-1-醇、醛和乙酸酯双键位置异构体

引言随着国际上神经网络研究的兴起,该法也逐步应用于化学模式识别领域。国内外很早就开展了化学模式识别的理论和应用研究,取得了很好的成果。最近,我们首次用神经网络进行了高温超导体制备条件的识别,成功率达100％。而后,又开展了生物制品(冠心病患者与健康人的血液样品)的多变量分析,利用神经网络探索微量金属元素与疾病(冠心病)之间的关     

7-十二烯基-8-羟基喹啉的合成

7-十二烯基-8-羟基喹啉是一种新型螯合型萃取剂(Kelex 100的主要成份)。它是(1a)和(1b)异构体的混合物。本文报道了利用1-氯-1,1,3,3-四甲基丁烷与过量丁二烯-[1,3]的1,4加成反应合成重要中间体1-氯-5,5,7,7-四甲基辛烯-[2](3)的方法,此法具有室温、常压、产率高、设备简单、操作方便等优点。     

7-取代-8-羟基喹啉的合成

由2-氯辛烷与8-羟基喹啉钠反应制得7-(1′-甲基庚基)-8-羟基喹啉.用长碳链脂肪醛在碱性介质中与8-羟基喹啉反应,合成了一系列7-烯基-8-羟基喹啉(1);对不同结构长碳链脂肪醛合成作了研究.     

双环[10,3,0]十五碳-1(12)-烯-13-酮系列化合物的合成

以多聚磷酸为催化剂,通过环十二碳烯和α,β-不饱和酸的反应,合成了十个双环[10.3.0]十五碳-1(12)-烯-13-酮系列化合物,研究了合成反应的条件及产物的分离提纯方法,并测定了它们的紫外光谱、红外光谱以及~1H核磁共振谱。     

羟基功能化3,4-聚2-芳基-1,3-丁二烯的合成

采用(C₁₃H₈CH₂CH₂(NCHCCHN)■)Lu(CH₂SiMe₃)₂ (1)、Ph₂P(=NDip)(NDip)Lu■(THF)(Dip=■)、Ph₂P(=NDip)(■)Lu(CH₂SiMe₃)₂(THF)(3)和Ph₂P(=NDip)(■)Sc(CH₂SiMe₃)₂(THF)(4) 4种稀土催化剂,催化2-对甲基苯基-1,3-丁二烯(2-MPBD)均聚合.通过核磁共振(NMR)、凝胶渗透色谱(GPC)和示差扫描量热法(DSC)等对聚合物的微观结构和热性能进行了表征.催化剂1对2-MPBD聚合活性低,3,4-选择性低(65.2%);催化剂2几乎无催化活性;催化剂3和4表现出非常高的催化活性(2 min转化率达100%)和3,4-选...     

镍及钯错合物催化1,3-双烯化合物之加成反应

在镍及钯错化合物之催化下, 有机试剂RX, R'MX可与1,3-双烯反应, 形成1,4-加成之产物, 如所使用之1,3-双烯为环状且无PPh3存在下时, 加成的产物为顺式的构造。在2当量之PPh3下, 1,4-加成的产物为反应的构造。     

顺式环十二碳烯的构象

用分子力学法(MMX程序)计算了15-苯基双环[10,3,0]十五碳-1(12)-烯-13-酮,其分子中环十二碳烯部分的构象为能量最低的[l_(ene)2333],与X衍射结果吻合.同时,还计算了15个顺式环十二碳烯的构象,求出了它们的能量及结构参数,讨论了影响构象稳定性的因素.     

(Z)-1,2-二苯基-1,3-丁二烯与单取代的乙烯衍生物区位选择性环加成反应

(Z)-1,2-二苯基-1,3-丁二烯分别与甲基乙烯基酮、丙烯酰胺、丙烯腈以及丙烯醛发生环加成反应,均生成顺式和反式的连位取代环己烯衍生物。以IR、¹HNMR和MS鉴定了产物的结构。利用CNDO/2法计算了分子轨道能量和系数,由前线分子轨道理论解释了这种环加成反应的区位选择性。     

WITTIG反应的研究IV.苹果蠹蛾性信素E,E-8,10-十二烯-1-醇立体选择性的合成

通过二苯基烯丙基型叶立德与8-氧辛酸甲酯进行的WITTIG反应,合成苹果蠹蛾性信息素,E,E-8,10-十二碳二烯1-醇(5).这个反应的特点是反式选择性高,产物中E,E-异构体的含量可达95%以上.     

方便、有效的方法合成2-氨基-1,3-二氰基-4-芳基-5,6,7,8,9,10,11,12,13,14-十氢苯并[12]轮烯衍生物

芳香醛、环十二酮和丙二腈在苄胺存在下于95%乙醇中反应,方便地合成了一系列2-氨基-1,3-二氰基-4-芳基-5,6,7,8,9,10,11,12,13,14-十氢苯并[12]轮烯衍生物,苄胺作为碱催化此类的反应还未见报道.产物的结构经过红外、核磁和高分辨质谱确定.本方法具有反应时间短、容易操作、产率高等优点,是合成此类化合物的一条有效途径.     

The correlations of the enthalpy of vaporization and the surface tension of molecular liquids

Correlation relations based on Stefan's rule, which defined dependence between the enthalpy of vaporization, the surface tension, the molar volume and the molar mass of a substance, were obtained. For development of the correlation equations two computational procedures were used: a method of the least squares and a method of artificial neural networks. The method of artificial neural networks was shown to give somewhat better results than the linear least-squares procedure. The average deviation of the calculated values from the experimental ones did not exceed 6% for training set of substances and 10% for control set (the method of the least squares). For the method of artificial neural networks it is 3% and 8%, respectively.     

Simultaneous determination of composition of human urinary calculi by use of artificial neural networks

A new chemometric method, which uses artificial neural networks (ANN), is presented for determination of the composition of urinary calculi. The selected constituents were whewellite, weddellite, and uric acid from which approximately 40% of the urinary calculi obtained from Macedonia patients are composed. The results for the synthetic mixtures were better then those obtained by partial least squares (PLS) regression or by the principal component regression (PCR), because neural networks have better prediction capacity. The generalization abilities of the optimized neural networks were checked using the standard addition method on carefully selected real natural samples.     

氢键碱度的神经网络法计算

氢键在生命科学和化学等领域均起着十分重要的作用.化合物可以通过提供质子和接受质子等两种方式与其它化合物形成分子间氢键,其形成氢键的能力分别称为氢键酸度（hydrogen-bondacidity）和氢键碱度(hydrogen-bondbasicity).可以用正辛醇／水分配系数和环己烷／水分配系数的对数差（ΔlogP）[1]、溶剂化显色参数[2－3]等表示化合物形成氢键的能力,其中应用较多的是Abraham等[4]提出的总氢键酸度（）和总氢键碱度（）.但由于和要通过实验得到,繁琐不便,限制了它们的广泛应用.本文用神经网络法研究了理论计算得到的量子化学参数与之间的相…     

小波神经网络用于钼和钨的同时测定

介绍了小波神经网络的结构和算法，并首次用于混合试样中的钼和钨含量测定，在小波神经网络中，采用Ｍｏｒｌｅｔ母小波和一维搜索变步长共轭梯度优化方法，结果表明，小波神经网络优于ＢＰ网络，其预测含量的回收率在９６．０％～１０４．６％之间。     

A comparative study of laser induced breakdown spectroscopy analysis for element concentrations in aluminum alloy using artificial neural networks and calibration methods

A comparative study of analysis methods (traditional calibration method and artificial neural networks (ANN) prediction method) for laser induced breakdown spectroscopy (LIBS) data of different Al alloy samples was performed. In the calibration method, the intensity of the analyte lines obtained from different samples are plotted against their concentration to form calibration curves for different elements from which the concentrations of unknown elements were deduced by comparing its LIBS signal with the calibration curves. Using ANN, an artificial neural network model is trained with a set of input data of known composition samples. The trained neural network is then used to predict the elemental concentration from the test spectra. The present results reveal that artificial neural networks are capable of predicting values better than traditional method in most cases.     

Prediction of electrophoretic mobilities of sulfonamides in capillary zone electrophoresis using artificial neural networks

Artificial neural networks (ANNs) were successfully developed for the modeling and prediction of electrophoretic mobility of a series of sulfonamides in capillary zone electrophoresis. The cross-validation method was used to evaluate the prediction ability of the generated networks. The mobility of sulfonamides as positively charged species at low pH and negatively charged species at high pH was investigated. The results obtained using neural networks were compared with the experimental values as well as with those obtained using the multiple linear regression (MLR) technique. Comparison of the results shows the superiority of the neural network models over the regression models.     

QSAR modelling using combined simple competitive learning networks and RBF neural networks

The aim of this study was to propose a QSAR modelling approach based on the combination of simple competitive learning (SCL) networks with radial basis function (RBF) neural networks for predicting the biological activity of chemical compounds. The proposed QSAR method consisted of two phases. In the first phase, an SCL network was applied to determine the centres of an RBF neural network. In the second phase, the RBF neural network was used to predict the biological activity of various phenols and Rho kinase (ROCK) inhibitors. The predictive ability of the proposed QSAR models was evaluated and compared with other QSAR models using external validation. The results of this study showed that the proposed QSAR modelling approach leads to better performances than other models in predicting the biological activity of chemical compounds. This indicated the efficiency of simple competitive learning networks in determining the centres of RBF neural networks.     

Nondestructive determination of compound amoxicillin powder by NIR spectroscopy with the aid of chemometrics

Near-infrared (NIR) spectroscopy, in combination with chemometrics, enables nondestructive analysis of solid samples without time-consuming sample preparation methods. A new method for the nondestructive determination of compound amoxicillin powder drug via NIR spectroscopy combined with an improved neural network model based on principal component analysis (PCA) and radial basis function (RBF) neural networks is investigated. The PCA technique is applied to extraction relevant features from lots of spectra data in order to reduce the input variables of the RBF neural networks. Various optimum principal component analysis-radial basis function (PCA-RBF) network models based on conventional spectra and preprocessing spectra (standard normal variate (SNV) and multiplicative scatter correction (MSC)) have been established and compared. Principal component regression (PCR) and partial least squares (PLS) multivariate calibrations are also used, which are compared with PCA-RBF neural networks. Experiment results show that the proposed PCA-RBF method is more efficient than PCR and PLS multivariate calibrations. And the PCA-RBF approach with SNV preprocessing spectra is found to provide the best performance.     

神经网络与多元统计在复杂化学信息模式分类中的集成应用

天然有机物料的化学组成十分复杂，其组分通常高达几百种，从而使其组成与性质间自关系，即构效关系，难以分辨、识别和确定．例如白酒、香料的构效关系就难以用化学机理描述．通常采用数据处理方法，将其归结为模式分类问题．复杂化学信息模式分类问题有模三维数高、样本容量小等特点，采用人工神经网络（ANN）具有一定的优势和不足．文献［1，2］讨论了从网络训练算法出发，改善ANN运行性能的技术．本文拟从另一角度探讨该问题．1对输入模式的分析当物料的组分数增多时，构效关系的复杂程度将剧增．为提高神经网络的表达和处理有力，必…