有机磷酸酯类化合物气相色谱定量结构保留关系研究

采用分子电性距离矢量(MEDV)表征有机磷酸酯类化合物的分子结构,运用多元线性回归建立定量结构-色谱保留关系(QSRR)模型,同时采用逐步回归结合统计检测对模型进行变量筛选,建立了35个有机磷酸酯类化合物在3种不同固定相(OV-101,DB-1701和DB-WX)上气相色谱保留指数(RI)与MEDV的定量相关模型.在3种固定相上的QSRR模型的建模计算值复相关系数(R)、留一法(leave-one-out)交互校验复相关系数(QCV)分别为0.998 0和0.995 1(OV-101);0.996 3和0.989 6(DB-1701);0.993 7和0.984 1(DB-WX),表明模型具有良好估计能力与稳定性.     

On QSAR Study of Stereoselectivity for Wittig Reaction

1INTRODUCTION Witting reaction is an important and well-known organic reaction.In this reaction system,phospho-nium ylides react with aldehydes or ketones to gene-rate olefins and phosphonium oxides.Obviously,the position of double bond in olefins is exactly the po-sition of carbonyl group in the reactants,so there are no other position isomers in products.Due to this advantage,Witting reaction has been widely used in organic synthesis[1].Witting reaction could introduce double bond to c…     

Quantitative structure-retention relationships for ionic and non-ionic compounds in biopartitioning micellar chromatography

Quantitative structure-retention relationships, QSRRs, represent a powerful tool in chromatography. The objectives of QSRR studies are to predict the chromatographic retention behaviour of solutes based on their structural properties, to elucidate retention mechanisms, to optimize the separation of complex mixtures or to prepare experimental designs. In this paper, using the retention factors of 151 structurally unrelated solutes that cover a wide range of hydrophobicity, molecular size, hydrogen bonding properties and ionization degrees obtained in biopartitioning micellar chromatography (BMC) at different Brij35 micellar concentrations, several multivariate QSRR models are tested. It is demonstrated that the chromatographic retention of any molecule in BMC, independently of its family, can be adequately described by its hydrophobicity (expressed as log P) and its anionic and cationic total molar charge (expressed as alpha(A) and alpha(B)).     

烃类化合物在不同色谱柱上的定量结构-保留相关性研究

运用量子化学中的AMI方法计算烃类化合物的分子结构描述参数,借助逐步回归法建立了烃类化合物在不同极性色谱柱上的QSRR模型。结果表明：烷烃、烯烃、二烯烃类化合物在不同极性的色谱柱上的色谱保留与其分子结构描述参数之间具有较好的线性关系,烃类化合物在不同极性固定相上的保留主要与溶质分子的MR有关,即与溶质分子的色散力有关。随着溶质分子的不饱和度的增加,或固定相极性的增强,溶质分子与固定相之间的电荷传递作用随之增强。而且,烃类化合物在不同极性固定相上的色谱保留的QSRR模型均可用量化参数HOMO、LUMO、EICE以及MR参数来描述。所建立的在不同极性色谱柱上的烃类化合物的色谱保留QSRR模型预测烃类化合物的色谱保留值时具有较好的稳定性和准确性。     

应用分子电距矢量对地笋中挥发油化学成分色谱保留值分析

采用分子电性距离矢量(MEDV)表征地笋中挥发油化学成分的分子结构,并对其气相色谱保留时间进行了系统的定量结构-色谱保留关系(QSRR)研究。在变量筛选的基础上建立了多个挥发油化学成分QSRR模型,相关系数均在0.90以上。通过严格的统计检验表明所建模型具有良好的稳定性与预测能力。     

Computational Investigations about the Effects of Hetero‐molecular Aggregation on Bioactivities: a Case of Neonicotinoids and Water

Molecular aggregation state of bioactive compounds plays a key role in bio‐interactive procedure. Diverse aggregation states of bioactive compounds contribute to different biological or chemical properties. Water‐bridge, as the simple hetero‐molecular aggregation, has been found bridging the binding between many bioactive compounds and their targets through hydrogen bonding network, e.g. in the recognition of neonicotinoids with insect nAChRs. To better understanding the roles of water‐bridge on bioactivities of compounds, an approach of hetero‐dimeric aggregation with water was proposed. Quantitative structure‐activity relationship (QSAR) and pharmacophore modeling investigations were applied on 19 neonicotinoids, as well as their aggregates with water. The aggregate‐based CoMSIA, PHASE and linear QSAR models presented better statistical significance and predictabilities than the monomer ones, which indicated that the bioactivities correlated with the aggregate properties and water bridged hydrogen bond of the active site. All results revealed the essential roles of water‐bridge in ligand recognition, which should be considered in future ligand design and optimization.     

多氯萘的气相色谱保留指数与其结构参数的定量关系

在B3LYP/6-31G*水平上计算了76个多氯萘分子,将计算得到的结构参数和热力学参数作为理论描述符引入到与气相色谱保留指数(RI)相关的多元回归分析中,建立了拟合度高、物理意义明确、预测能力强的保留时间-结构参数的相关方程(模型Ⅰ)(r2=0.9957);再以氯原子的取代个数和相互位置作为理论描述符,得出另一模型(模型Ⅱ)(r2=0.9967)。找出了影响多氯萘保留时间的主要因素。     

A Quantitative Structure–Activity Relationship Study of Calpeptin (Calpain Inhibitor) as an Anticancer Agent

Calpeptin analogs show anticancer properties with inhibition of calpain. In this work, we applied a quantitative structure–activity relationship (QSAR) model on 34 calpeptin derivatives to select the most appropriate compound. QSAR was employed to generate the models and predict the more significant compounds through a series of calpeptin derivatives. The HyperChem, Gaussian 09, and Dragon software programs were used for geometry optimization of the molecules. The 2D and 3D molecular structures were drawn by ChemDraw (Ultra 16.0) and Chem3D (Pro16.0) software. The Unscrambler program was used for the analysis of data. Multiple linear regression (MLR‐MLR), partial least‐squares (MLR‐PLS1), principal component regression (MLR‐PCR), a genetic algorithm‐artificial neural networks (GA‐ANN), and a novel similarity analysis‐artificial neural network (SA‐ANN) method were used to create QSAR models. Among the three MLR models, MLR‐MLR provided better statistical parameters. The R² and RMSE of the prediction were estimated as 0.8248 and 0.26, respectively. Nevertheless, the constructed model using GA‐ANN revealed the best statistical parameters among the studied methods (R² test = 0.9643, RMSE test = 0.0155, R² train = 0.9644, RMSE train = 0.0139). The GA‐ANN model is found to be the most favorable method among the statistical methods and can be employed for designing new calpeptin analogs as potent calpain inhibitors in cancer treatment.     

咪唑啉衍生物缓蚀剂的定量构效关系及分子设计

采用量子化学密度泛函理论(DFT)及线性回归分析方法, 对十一烷基咪唑啉衍生物缓蚀剂抗H2S、CO2腐蚀性能进行了定量构效关系(QSAR)研究. 通过回归分析, 筛选出了影响缓蚀剂缓蚀性能的主要因素, 建立了QSAR模型, 并使用留一法交叉验证对模型的稳定性及预测能力进行了分析. 结果表明, 电子转移参数△N、咪唑环上非氢原子静电荷之和∑Qring及分子极化率α对咪唑啉类缓蚀剂的缓蚀性能有很大的贡献, 所得模型的拟合相关系数(R2)和交叉验证相关系数(q2)分别为0.924 和0.917, 模型对此类缓蚀剂抗H2S、CO2腐蚀性能具有较好的预测效果. 应用QSAR研究结果进行了分子设计, 在理论上提出了一些具有较高抗H2S、CO2腐蚀性能的新型咪唑啉衍生物, 为实验工作者合成新型缓蚀剂提供理论参考.     

An alignment‐free methodology for modelling field‐based 3D‐structure activity relationships using inductive logic programming

Traditional 3D‐quantitative structure–activity relationship (QSAR)/structure–activity relationship (SAR) methodologies are sensitive to the quality of an alignment step which is required to make molecular structures comparable. Even though many methods have been proposed to solve this problem, they often result in a loss of model interpretability. The requirement of alignment is a restriction imposed by traditional regression methods due to their failure to represent relations between data objects directly. Inductive logic programming (ILP) is a class of machine‐learning methods able to describe relational data directly. We propose a new methodology which is aimed at using the richness in molecular interaction fields (MIFs) without being restricted by any alignment procedure. A set of MIFs is computed and further compressed by finding their minima corresponding to the sites of strongest interaction between a molecule and the applied test probe. ILP uses these minima to build easily interpretable rules about activity expressed as pharmacophore rules in the powerful language of first‐order logic. We use a set of previously published inhibitors of factor Xa of the benzamidine family to discuss the problems, requirements and advantages of the new methodology. Copyright © 2007 John Wiley & Sons, Ltd.     

高效液相色谱法构建定量结构-保留关系预测中药肝肾毒性组分的正辛醇-水分配系数值

构建了定量结构-保留关系(QSRR)测定马兜铃酸A、马兜铃酸B、马兜铃内酰胺及白黎芦醇的正辛醇-水分配系数(K_(ow))。采用反相高效液相色谱(RP-HPLC)法,以甲醇-水为流动相,以16种已知K_(ow)值的酸性和中性苯系物为模型化合物,以保留时间两点校正法(DP-RTC)校正保留时间,并由Snyder-Soczewinski方程得100%水相保留因子k_w,建立了表观正辛醇-水分配系数K_(ow)″与k_w的定量关系(QSRR模型),并对模型进行了内、外部验证。结果显示,不同pH下的QSRR模型线性相关性良好(相关系数R~2为0.980~0.987),内部验证(交叉验证相关系数R_(cv)~2为0.982~0.988)和外部验证结果(6种验证化合物的相对误差RE为0.6%~10.9%)令人满意。将建立的QSRR模型应用于中药中4种潜在肝肾毒性化合物的K_(ow)测定,并与软件计算值、摇瓶法(SFM)实验值比较,结果显示该方法准确性更高,且简单快捷。该文提出的采用中性及酸性苯系物建立QSRR模型,通过对结构与性质相似的中药材组分进行RP-HPLC分析,得到各待测组分的保留时间即可获得其K_(ow)值的简便策略,解决了中药组分复杂且难以分离、无法通过SFM测定其K_(ow)值的问题,为通过定量-构效关系(QSAR)模型实现快速预测中药组分的肝肾毒性提供了可靠的K_(ow)数据。     

5-芳基乙内酰脲类化合物QSERR研究

用多元线性回归分析对50个在Pirkle型手性固定相色谱柱上手性拆分的乙内酰脲类化合物进行了QSRR（quantitative structure-rentention relationship）和QSERR（ quantitative-structure enantioselective rentention relationship）研究 .所选的2D和3D描述参数由分子力学和量子化学计算得到.回归结果表明,R和S异构体与识别剂的相互作用的确存在差异.这些方程不仅有助于推测被识别剂和识别剂之间的结合方式,还可以定量的预测结构相近的类似物的拆分可能性,为设计合成新的识别剂和被识别剂都提供了一定的理论依据.     

抗癌性吲哚喹唑啉衍生物的定量构效关系

用量子化学密度泛函理论(DFT)、分子力学(MM＋)及回归分析方法,对一系列抗癌性吲哚喹唑啉衍生物进行了定量构效关系(QSAR)的研究.通过回归分析,筛选了影响抗癌活性的主要因素,建立了定量构效关系方程.结果表明,化合物的最低未占据分子轨道(LUMO)与最高占据分子轨道(HOMO)之间的能量差(ΔεL-H)、分子的疏水性(lgP)以及环D上的总电荷(ΣQD)和环D上R1取代基的第一个原子的净电荷(QFR1)是影响化合物抗癌活性的主要因素.所得模型对化合物抗癌活性有较好的预测效果. 同时, 与ΔεL-H密切相关的LUMO轨道能量及共轭平面面积对药物的DNA-结合及其活性起着十分重要的作用,可通过选取具有较强的拉电子性质同时又能与本系列化合物的骨架形成更大共轭体系的取代基R1,设计抗癌活性较高的化合物.