辨识药物定量构效关系的模糊神经网络方法研究

提出一种基于遗传算法的新型模糊神经网络方法,用于计算Benzodiazepines(BZs)类药物的定量构效关系.这类模糊神经网络综合了神经网络、遗传算法与模糊逻辑的各自优势,具有优良的定量构效关系辨识能力,其学习速度较快,不易陷入局部最小区域;网络知识以模糊语言变量的形式加以表达,不仅易于理解,而且能有效地利用已有的专家经验.一旦通过学习获得规律后,不仅能很好地预测化合物的活性,还能对后续的药物分子设计提供有益的理论指导.     

跨接VTSRK方程计算烃类的pVT性质

烃类pVT性质的精细表征对能源动力、化工等领域应用有重要价值,临界区热力性质描述是难点之一.本文建立了烷烃(C1-C20)的跨接比容平移Soave-Redlich-Kwong(SRK)(跨接VTSRK)状态方程,在SRK状态方程的基础上引入了比容平移和跨接方法,以改善饱和液相密度和近临界区域热力学性质的计算精度,方程参数被表达为物质临界参数和偏心因子的函数.比较结果表明,跨接方程对烷烃(C1-C20)饱和蒸气压、饱和气相密度、饱和液相密度的计算平均偏差分别为1.01%、1.83%和0.93%,显著优于原方程,单相区和近临界区的pVT性质计算精度也比原状态方程有较大改善.进一步将方程推广到环烷烃(环丙烷、环戊烷和环己烷)和苯、甲苯的计算,也获得了较好效果,验证了方程的预测能力.     

跨接VTSRK方程计算烃类的pVT性质

烃类pVT性质的精细表征对能源动力、化工等领域应用有重要价值,临界区热力性质描述是难点之一.本文建立了烷烃（C1-C20）的跨接比容平移Soave-Redlich-Kwong（SRK）（跨接VTSRK）状态方程,在SRK状态方程的基础上引入了比容平移和跨接方法,以改善饱和液相密度和近临界区域热力学性质的计算精度,方程参数被表达为物质临界参数和偏心因子的函数. 比较结果表明,跨接方程对烷烃（C1-C20）饱和蒸气压、饱和气相密度、饱和液相密度的计算平均偏差分别为1.01%、1.83%和0.93%,显著优于原方程,单相区和近临界区的pVT性质计算精度也比原状态方程有较大改善. 进一步将方程推广到环烷烃（环丙烷、环戊烷和环己烷）和苯、甲苯的计算,也获得了较好效果,验证了方程的预测能力.     

碳氢化合物燃烧中间体Lennard-Jones系数的计算

在已有的基团贡献法公式的基础上,提出了一种新的基团贡献法公式,并通过拟合250种化合物(包括185种稳定化合物临界性质的实验值和65种自由基临界性质的计算值)的临界性质得到了40种基团的贡献值,并用于预测未知化合物的临界性质.选取了训练集以外的、有临界性质实验值的30种化合物作为独立测试集,用于验证所建模型对临界性质的预测能力,T_C和P_C平均绝对偏差分别为8.52%和16.83%.结果表明,预测结果和实验值相吻合,该模型可以用于大分子化合物及自由基的临界性质预测.根据临界性质与Lennard-Jones(L-J)系数的经验关系式,预测了碳氢化合物燃烧中间体的L-J系数,得到独立测试集46种碳氢化合物的L-J系数,与文献值接近,T_C和P_C的平均绝对偏差分别为9.88%和9.96%.比较了训练集中烷烃自由基·C_6H_(13)、烯烃自由基·C_5H_9和炔烃自由基·C_5H_7同分异构体的L-J系数,同时,将己烷自由基·C_6H_(13)与相似的邻近烷烃C_6H_(14)的L-J系数进行比较,发现同分异构体之间或相似化合物之间L-J系数有较大偏差.此外,对缺少L-J系数的114种常见碳氢化合物自由基进行了预测.这对于碳氢化合物的燃烧模拟及基元反应中压强相关的速率常数计算有重要意义.     

摩尔响应值与拓扑指数的关系

考察了链烷烃、链烷醇、酮和苯系列化合物的摩尔响应值与拓扑指数的关系,结果表明两者之间存在良好的线性关系。     

饱和醇结构-保留定量相关的人工神经网络模型

以拓扑指数为结构描述符，用基于Levenberg-Marquardt优化的BP神经网络建立了醇类化合物的结构与色谱保留值的相关性模型，用于未知醇类化合物在SE－30和OV－3两根色谱柱上保留指数的同时预测，其学习速率优于文献中普通BP神经网络法，预测准确度与普通BP神经网络法接近，但优于多元线性回归法，因而是一种较好的预测有机化合物气相色谱保留指数的方法。     

直链烷烃结构型和凝聚型性质的递变规律研究

用图示法和非线性规划的方法对直链烷烃同系物最高成键分子轨道能级、最低成键分子轨道能级,电离电位、氧化半波电位、正常沸点、正常熔点、临界压力、临界温度、密度、折光率、表面张力和粘度等12种结构型性质和凝聚型性质的变化规律进行研究,结果发现直链烷烃结构型性质和凝聚型性质一般能遵守同系对数递变规律,各种结构型性能和凝聚型性能均与对数递变函数呈优良的相关性,相关系数均大于0.99．用同系对数递变规律对直链烷烃各种性能进行预测的结果表明,除少数同系列起始化合物的偏差较大外,大多数预测值与实验值非常吻合,实验散点几乎与对数递变函数曲线相重合．     

长链正烷烃的受限结晶研究进展

聚合物的结晶过程和最终凝聚态结构直接影响材料的加工使用性能.作为高分子材料的最大品种,聚烯烃由于分子量大,分子量分布较宽,结晶过程中形成多种亚稳态,因而从分子水平上阐明其结晶机理存在困难.与聚乙烯链结构相似的长链正烷烃可作为聚烯烃的模型化合物,研究其受限结晶行为能为复杂的聚合物受限结晶提供理想的模型体系.长链正烷烃的受限空间可以分为一维受限薄膜、二维受限微孔、三维受限微乳液或微胶囊等.相对于本体,长链正烷烃在每个受限体系中的结晶行为各不相同,这主要来源于受限体系对成核、结晶以及相转变的影响.本文重点综述了长链正烷烃在3种受限体系中的结晶特点,并结合各个体系中聚合物的结晶特点,阐述了长链正烷烃作为聚合物模型化合物的合理性.     

摩尔响应值与分子连接性指数关系的再研究

对链烷烃、链烷醇、苯系列和酮类化合物选择适当的分子连接性指数,用摩尔响应值对它们进行多元线性回归,结果表明两者之间存在良好的相关性。     

用分子子图对烷烃摩尔响应值的估计与预测

提出了一种新的烷烃拓扑子图表示方法,并结合多元线性回归算法和反传神经网络算法,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ｒ＝０．９８９。     

Quantitative fuzzy neural network for analytical determination

A quantitative fuzzy neural network (Q-FNN) for pattern recognition in analytical determination is reported in this paper. The fuzzy neural network (FNN) combines a fuzzy logic system with an artificial neural network (ANN) so that it has both advantages of a high training speed and strong anti-interference. Importantly, the analytical concept of relative error (RE) in quantitative determination has been integrated into FNN so that the Q-FNN provides a very good quantitative capability in chemical analysis, and prevents the system from an over-fitting problem. The logarithm curve with noise in terms of analytical response versus concentration is calibrated by trained FNN and a close approximation to the ideal one without noise is obtained. The Q-FNN has been applied to the concentration determination of freon in the presence of interference gases. The prediction error for a test set in quantification is less than 10% while no qualitative mistake is observed, implying that the quantitative FNN has sustained the feature of pattern recognition. The results indicate that the Q-FNN has obvious advantages not only in converging speed, but also in the quantitative accuracy over the ANN.     

一类基于模糊神经元的复杂非线性化学模式识别方法

针对模式类别边界曲折而模糊的复杂化学模式分类问题,提出一种化学模式模糊分类方法,并给出其模糊神经元分类器设计和网络训练算法,使模糊神经元分类器具有学习功能.以一个应用实例检验了该方法的实效.     

Neural network studies : Part 3. Prediction of partition coefficients

In a previous paper (N. Bodor, A. Harget and M.-J. Huang, J. Am. Chem. Soc., 113 (1991) 9480) we demonstrated the utility of a neural network approach in the estimation of the aqueous solubility of organic compounds. This approach has now been extended to the prediction of partition coefficients. A training set of AM1 calculated properties and experimental values for 302 compounds was used and, after training, the neural network was tested for its ability to predict the partition coefficients of 21 compounds not included in the training set. We also tested six more compounds with molecular properties out of the training set property range. A comparison was made with the results obtained from a previous study which had used a regression analysis approach (N. Bodor and M.-J. Huang, J. Pharm. Sci., 81 (1992) 272). The neural network results compared favorably with those given by the regression analysis approach, both for the training set and for the new compounds.     

Quantitative structure chromatography relationships in reversed-phase high performance liquid chromatography: Prediction of retention behaviour using theoretically derived molecular properties

Summary The use of theoretically calculated molecular properties as predictors for retention in reversed-phase HPLC has been explored. HPLC retention times have been measured for a series of 47 substituted aromatic molecules in three solvent mixtures and steric and electronic properties of these compounds have been derived using semi-empirical molecular orbital and empirical theoretical methods. A subset of the experimental data (a training set) was used to derive property-retention time relationships and the remaining data were then used to test the predictive capability of the methods.Good retention time prediction was possible using derived regression equations for individual solvents and after including solvent parameters it was possible to predict retention for all solvents using a single equation. This method showed that the most useful properties were calculated log P and the calculated dipole moment of the solutes, and the calculated solvent polarisability. In addition, 90% of the data were used to train an artificial neural network and the remaining 10% of the data used to test the network; excellent prediction was obtained, the neural network approach being as successful as the regression analysis.     

A comparison between neural network method and semi empirical equations to predict the solubility of different compounds in supercritical carbon dioxide

Accuracy of seven semi empirical equations for the estimation of solubility of 30 different compounds in supercritical carbon dioxide has been compared with a new neural network method. To base this comparison on a fair basis, a unique set of experimental data was used for both optimization of semi empirical equations’ parameters and training, validation and testing of neural network. Results showed that neural network method with an average relative deviation of about 5.3% was more accurate than the best semi empirical equation with an average relative deviation of about 15.96% for same compounds. It was also found that the average relative deviation of semi empirical equations varies sharply among different compounds, while this quantity is less dependent on material type for neural network method.