应用分子电性距离矢量预测烷烃和一元醇的折光指数

应用分子电性距离矢量对81个烷烃、22个一元醇进行了结构表征,通过多元线性回归与逐步回归的方法建立了分子电性距离矢量与折光指数的定量结构性质模型,模型的相关系数分别为0.980和0.979.采用留一法对模型进行交互检验复相关系数R2cv分别为0.927和0.898.说明定量结构性质模型具有很好的稳定性和预测功能.     

原子电性作用矢量和杂化状态指数用于氨基酸核磁共振碳谱模拟

提出了用于表征分子局部化学微环境及原子所处杂化状态的结构描述子:原子电性作用矢量(AEIV)和原子杂化状态指数(AHSI),将其应用于20个天然氨基酸103个碳原子13C核磁共振模拟中,取得满意结果。模型计算值、留一法(LOO-CV)交互校验预测值和新颖的留一分子法(LMO)交互校验预测值的复相关系数分别为r=0.9948、0.9940和0.9924。进一步使用4个非天然氨基酸化学位移值来测试该模型的预测能力,预测复相关系数为r=0.9940。     

分子电性距离矢量用于三嗪类化合物的构效关系研究

采用分子电性距离矢量(Molecular Electronegativity Distance Vector,MEDV)表征了三嗪类化合物的分子结构,并运用多元线性回归(Multiple Linear Regression,MLR)建立了该类化合物结构与其发光菌和大型蚤毒性的定量结构-毒性相关(Quanti-tative Structure-Toxicity Relationship,QSTR)模型,同时采用留一法交互检验对所建模型进行了分析和验证,建模计算值的相关系数R分别为0.970和0.952,留一法交互检验预测值的相关系数RLOO分别为0.917和0.921,并进一步阐述了结构与毒性之间的关系。结果表明,三嗪环上π电子离域程度减小有利于毒性增加,侧链N上取代基数目增加,化合物毒性减小。为进一步预测该类化合物的毒性,进行药物筛选提供了有效的理论依据。     

分子电性距离矢量用于FCC汽油中硫化物的QSRR研究

采用拓扑结构描述符中的分子电性距离矢量(MEDV),对催化裂化(FCC)汽油中48种硫化物在PONA柱上的气相色谱保留指数值(RI)建立多元线性回归模型和神经网络BP模型,并进行模型对比。结果表明,MEDV能很好分辨FCC汽油中不同硫化物以及同种硫化物异构体,由此建立的定量结构–保留相关关系的多元线性回归(MLR)模型和神经网络BP模型都具有较好的稳定性和良好预测能力,而非线性BP模型优于MLR模型的预测能力。     

分子电性距离矢量用于稠环芳烃液相色谱保留值的QSPR研究

采用分子电性距离矢量(Molecular Electronegativity Distance Vector,MEDV)表征稠环芳烃类化合物的分子结构.分别运用多元线性回归(Multiple Linear Regres-sion,MLR)和偏最小二乘回归(PLS)建立了稠环芳烃类化合物结构与其液相色谱(LC)保留值的定量结构一性质关系(QSPR)模型,同时采用内部及外部双重验证的办法对所建模型稳定性能进行分析和验证,建模计算值、留一法交互检验预测值和外部样本预测值的复相关系数Rcum、RLOO、Qext分别为0.9970,0.9950,0.9925(MLR);0.9930,0.9790,0.9917(PLS).结果表明,MEDV能较好地表征该类分子结构信息,所建QSPR模型具有良好的稳定性和预测能力.为稠环芳烃类化合物分离、纯化、检测等方法的建立,提供有效的理论依据.     

分子电性距离矢量用于酯的定量结构-色谱保留相关研究

分子电性距离矢量(MEDV)是一种描述分子二维结构的拓扑描述子,由4种类型原子间相互作用的10个元素组成.通过引入原子属性和原子类型的概念构建的MEDV,适用于描述含多个杂原子、饱和键与不饱和键、环和非环等分子结构特征.利用MEDV对73个酯在不同固定相、不同柱温下219个样本的气相色谱保留指数值(R1)建立多元线性回归模型,其相关系数r=0.9957,继以留一谣(Leave-one-out)进行交互检验,相关系数rCV=0.0950.建模结果显示,MEDV具有很好的结构选择性,所建定量结构-保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了酯类化合物在不同固定相、不同柱温下气相色谱保留指数的变化规律.     

基于分子电性距离矢量预测有机污染物的生物富集因子

基于分子电性距离矢量描述子(MEDV)表征236种有机污染物的分子结构, 应用最佳子集回归与偏最小二乘方法建立化合物的生物富集因子与其分子结构之间的相关QSAR模型. 结果显示, 影响其生物富集活性的分子结构碎片为—CH₂、—X、—C≮、—C≮、—O—, 所建立模型具有较高的估计相关系数及LOO(leave-one-out)检验相关系数, 表明模型具有良好估计能力与稳定性, 同时应用训练集样本构建的QSAR模型预测外部检验集, 表明训练集模型具有良好的预测能力.     

分子电性距离矢量用于多溴联苯醚分子结构表达及色谱保留时间预测

应用分子电性距离矢量(MEDV)对多溴联苯醚(PBDEs)的209种同系物进行结构表征.通过多元线性回归的方法,建立了PBDEs定量结构-色谱保留(QSRR)关系的6个变量和5个变量的两种模型.两种模型的建模计算值复相关系数R均为0.995;用留一法(LOO)进行了交互检验,其复相关系数(R2cv)分别为0.987和0...     

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。     

Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning

High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification remains challenging due to the lack of orthogonal sources of information (e.g., unique fragments). Collision cross section (CCS) values measured by ion mobility spectrometry (IMS) offer an additional identification dimension to increase the confidence level. Thanks to the advances in analytical instrumentation, an increasing application of IMS hybrid with high-resolution mass spectrometry (HRMS) in NTS has been reported in the recent decades. Several CCS prediction tools have been developed. However, limited CCS prediction methods were based on a large scale of chemical classes and cross-platform CCS measurements. We successfully developed two prediction models using a random forest machine learning algorithm. One of the approaches was based on chemicals’ super classes; the other model was direct CCS prediction using molecular fingerprint. Over 13,324 CCS values from six different laboratories and PubChem using a variety of ion-mobility separation techniques were used for training and testing the models. The test accuracy for all the prediction models was over 0.85, and the median of relative residual was around 2.2%. The models can be applied to different IMS platforms to eliminate false positives in small molecule identification.     

A new two-dimensional approach to quantitative prediction for collision cross-section of more than 110 singly protonated peptides by a novel moecular electronegativity-interaction vector through quantitative structure-spectrometry relationship studies

Based on two-dimensional topological characters, a novel method called molecular electronegativity-interaction vector (MEIV) is proposed to parameterize molecular structures. Applying MEIV into quantitative structure-spectrometry relationship studies on ion mobility spectrometry collision cross-sections of 113 singly protonated peptides, three models were strictly obtained, with correlative coefficient r and leave-one-out cross-validation q of 0.983, 0.979, 0.981, 0.979 and 0.980, 0.978, respectively. Thus, the MEIV is confirmed to be potent to structural characterizations and property predictions for organic and biologic molecules. Translated from Chinese Journal of Analytical Chemistry, 2006, 34(6) (in Chinese)     

Identification and Structural Characterization of Novel Chondroitin/Dermatan Sulfate Hexassacharide Domains in Human Decorin by Ion Mobility Tandem Mass Spectrometry

Chondroitin sulfate (CS) and dermatan sulfate (DS) are found in nature linked to proteoglycans, most often as hybrid CS/DS chains. In the extracellular matrix, where they are highly expressed, CS/DS are involved in fundamental processes and various pathologies. The structural diversity of CS/DS domains gave rise to efforts for the development of efficient analytical methods, among which is mass spectrometry (MS), one of the most resourceful techniques for the identification of novel species and their structure elucidation. In this context, we report here on the introduction of a fast, sensitive, and reliable approach based on ion mobility separation (IMS) MS and MS/MS by collision-induced dissociation (CID), for the profiling and structural analysis of CS/DS hexasaccharide domains in human embryonic kidney HEK293 cells decorin (DCN), obtained after CS/DS chain releasing by β-elimination, depolymerization using chondroitin AC I lyase, and fractionation by size-exclusion chromatography. By IMS MS, we were able to find novel CS/DS species, i.e., under- and oversulfated hexasaccharide domains in the released CS/DS chain. In the last stage of analysis, the optimized IMS CID MS/MS provided a series of diagnostic fragment ions crucial for the characterization of the misregulations, which occurred in the sulfation code of the trisulfated-4,5-Δ-GlcAGalNAc[IdoAGalNAc]₂ sequence, due to the unusual sulfation sites.     

A Strategy for Identification and Structural Characterization of Compounds from Plantago asiatica L. by Liquid Chromatography-Mass Spectrometry Combined with Ion Mobility Spectrometry

Plantago asiatica L. (PAL) as a medicinal and edible plant is rich in chemical compounds, which makes the systematic and comprehensive characterization of its components challenging. In this study, an integrated strategy based on three-dimensional separation including AB-8 macroporous resin column chromatography, ultra-high performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS), and ultra-high performance liquid chromatography-mass spectrometry with ion-mobility spectrometry (UHPLC-IM-MS) was established and used to separate and identify the structures of compounds from PAL. The extracts of PAL were firstly separated into three parts by AB-8 macroporous resin and further separated and identified by UHPLC-Q-TOF MS and UHPLC-IM-MS, respectively. Additionally, UHPLC-IM-MS was used to identify isomers and coeluting compounds, so that the product ions appearing at the same retention time (RT)can clearly distinguish where the parent ion belongs by their different drift times. UNIFI software was used for data processing and structure identification. A total of 86 compounds, including triterpenes, iridoids, phenylethanoid glycosides, guanidine derivatives, organic acids, and fatty acids, were identified by using MS information and fragment ion information provided by UHPLC-Q-TOF MS and UHPLC-IM-MS. In particular, a pair of isoforms of plantagoside from PAL were detected and identified by UHPLC-IM-MS combined with the theoretical calculation method for the first time. In conclusion, the AB-8 macroporous resin column chromatography can separate the main compounds of PAL and enrich the trace compounds. Combining UHPLC-IM-MS and UHPLC-Q-TOF MS can obtain not only more fragments but also their unique drift times and RT, which is more conducive to the identification of complex systems, especially isomers. This proposed strategy can provide an effective method to separate and identify chemical components, and distinguish isomers in the complex system of traditional Chinese medicine (TCM).     

支持向量机用于高分子聚合物的折射率预测

支持向量机;定量构性相关;高聚物;折射率