从药物的三维分子结构预测人体小肠吸收

在口服药物的发展过程中，人体小肠吸收的预测是候选药物设计、优化和选择的一个主要目标.VolSurf/GRID计算方法作为一个新的工具被用来预测被测化合物的人体小肠吸收，以及测定人体小肠吸收所必需的重要的分子特征.被测化合物包括112个结构不同的类似药物的化合物.使用偏最小二乘判别分析方法在实验数据和人体小肠吸收的理论分子特征之间建立相关性.建立的两个模型之间具有较高的一致性.小肠吸收实验数据与分子特征之间好的相关性（r2=0.82, q2=0.67）表明,从化合物的三维分子结构能够预测人体小肠吸收.有利于人体小肠吸收的药物分子特征包括,分子量中心与亲水区重心的不平衡性，较大的疏水区域以及分子内较少的氢键给体.     

PLS变量筛选法用于有机物透聚乙烯膜性能QSAR研究

随着大量分子描述符应用于QSAR/QSPR,如何筛选出具有良好稳定性和预测能力的描述符集,成为亟待解决的一个瓶颈问题.将63个有机化合物的1664个描述符经过初步预选后,利用偏最小乘(PLS)方法进行变量筛选,获得42个重要描述符;随机选择43个有机物,针对透聚乙烯膜性能进行训练研究,得优良估计能力和良好稳定性模型(A=6,r2=0.9647,RMSE=0.213,q2=0.8364,RMSV=0.467);对模型外部20个有机物进行预测,表明模型具有良好预测能力(rp2=0.9306,RMSP=0.326).PLS变量筛选法可以快速有效地筛选与活性密切相关的重要描述符,进而构建具有良好稳定性和预测能力的QSAR模型.     

基于近红外光谱和OPLS-DA的不同牌号卷烟分类识别方法研究

为了对卷烟牌号进行准确分类鉴别,提出了一种基于近红外光谱(NIRS)分析技术结合有监督的模式识别快速鉴别卷烟牌号的新方法。利用标准正态变量变换(SNV)、多元散射校正(MSC)、一阶导数(FD)、二阶导数(SD)和Savitzky-Golay平滑(SG)及其相结合的光谱预处理方法对烟丝光谱进行预处理,通过近红外光谱结合主成分分析(PCA)、偏最小二乘判别分析(PLS-DA)和正交偏最小二乘判别分析(OPLS-DA) 3种模式识别方法对不同牌号烟丝进行分类识别研究,并采用分类识别正确率作为评价指标。实验结果表明:(1)烟丝近红外光谱主成分得分图交叉重叠,区分不明显,PCA无法识别出5种牌号的成品烟丝;(2)烟丝光谱经MSC+FD预处理后的PLS-DA模型可得到较好的识别效果,校正集和测试集的分类识别正确率分别为100%和98.3%;(3)烟丝光谱经MSC+SD预处理后的OPLS-DA模型的模式识别效果最好,模型对自变量拟合指数(R²X),因变量的拟合指数(R²Y)和模型预测指数(Q²)分别为0.485、0.907和0.74...     

离子液体中有机物溶解度的QSPR模型分析

采用偏最小二乘(PLS)分析方法对84个有机物在四种不同的离子液体中的溶解度进行基于VolSurf参数的定量构性关系(QSPR)研究,取得较好的结果.训练集模型对预测集具有良好的预测能力.参数分析表明有机物具有较大体积的亲水区域,对溶解度有利,且有机物与离子液体之间的相互作用能约为-0.84kJ·mol-1.一定的疏水性对溶解度也是有利因素,当离子液体具有小体积的疏水取代基,有机物具有不对称的局部疏水区域对溶解度有利,当离子液体具有大体积或多个疏水取代基,有机物较高的疏水体积对溶解度有利.多元线性回归(MLR)显示亲水参数W1最重要,表明分子的亲水性是影响有机物在离子液体中溶解的关键因素.     

基于化学模式识别技术的枳实HPLC定量指纹图谱研究

建立了枳实的高效液相色谱(HPLC)指纹图谱分析方法.色谱柱为Tnature-ACCHROM C18色谱柱(4.6 mmx250 mm,5 μm);以乙腈-0.5％甲酸水溶液为流动相进行梯度洗脱,结合液相色谱-四极杆飞行时间质谱(HPLC-QTOF-MS)联用技术对枳实指纹图谱中的共有峰进行鉴定;采用相似度评价、聚类分...     

低场核磁共振结合化学计量学方法快速检测掺假核桃油

以掺假核桃油样品为低场核磁共振检测对象,利用主成分分析法(PCA)和偏最小二乘回归法(PLSR)分析处理Carr-Purcell-Meiboom-Gill(CPMG)序列的核磁共振弛豫数据,旨在探求一种能快速检测核桃油品质的新方法。对几种常见掺假形式(掺入大豆油、玉米油、葵花油)的核桃油样品和纯核桃油样品进行检测和评价。实验结果表明:纯核桃油和掺入不同种类食用油的掺假核桃油在主成分得分图上可以得到很好的区分,且掺假样品随掺假比例在图中呈规律性分布;采用PLSR法对CPMG数据和实际掺假率进行回归,可实现对核桃油掺假水平的准确定量测定。方法快速、无损、准确,在食用油制品的品质控制及评价方面具有很大的应用潜力。     

氨基酸结构描述子矢量VHSE及其在肽QSAR中的应用

从20种天然氨基酸的50个物化性质出发，按照疏水、立体和电性特征将其分类后分别进行主成分分析，并将产生的得分矢量即VHSE(principal component score vector of hydrophilicity,steric，and electronic properties)作为氨基酸结构描述子用于肽的定量构效关系研究。与已有方法相比，VHSE描述子具有物化意义明确、结果更易解释等特点。应用该描述子并结合逐步回归变量筛选和偏最小二乘建模方法，在对苦味二肽和血管舒缓激肽促进剂等体系的定量构效关系研究中，均取得了优于已有文献的结果。     

基于灰狼算法的近红外光谱变量选择方法研究

基于群体智能的灰狼优化(GWO)算法具有参数少、结构简单、易于实现的优点,但在光谱领域的应用较少。该研究将GWO算法引入近红外光谱的变量筛选中,以玉米数据为例,考察了GWO算法中狼群性能、迭代次数、狼群数量及运算效率,并建立了偏最小二乘(PLS)模型对玉米样品中蛋白质、脂肪、水分以及淀粉含量的测定。结果显示,GWO算法运算效率很高,经过参数调优后建立PLS模型,其蛋白质、脂肪、水分及淀粉的保留变量数分别为19、19、14、34,预测均方根误差(RMSEP)从全波长PLS建模的0.245 8、0.122 4、0.339 8、1.105 8分别下降到0.147 7、0.080 1、0.176 2、0.739 8,分别下降了40%、35%、48%、33%,相关系数也相应地提高。因此,GWO算法不仅优化速度快,选择变量数少,还可以显著提高PLS模型的预测精度,是一种近红外光谱变量选择的有效方法。     

氨基酸描述子SZOTT用于多肽定量序效建模研究

在相关研究的基础上, 提出一新的氨基酸描述子SZOTT, 该描述子所含信息量大, 且操作简便. 将其用于两类肽体系序列表征, 用偏最小二乘和正交信号纠正-偏最小二乘建模, 获得较好的建模结果.     

基于局部加权偏最小二乘的近红外光谱分析方法研究

针对近红外光谱分析技术中分析对象非线性现象突出的情况,提出了一种新的模型计算方法——局部加权偏最小二乘法(LWPLS)。以安胎丸为研究对象,采用LWPLS算法进行其近红外定量模型的建立,并比较偏最小二乘法(PLS)与LWPLS两种算法建立定量模型的精度。结果测得两种算法建立的校正模型中,阿魏酸的模型相关系数(R²)分别为0.785 5、0.971 9,预测误差均方根(RMSEP)分别为0.126 6、0.043 8,相对预测误差(RE)分别为12.66%、9.18%;洋川芎内酯A的R²分别为0.886 4、0.964 9,RMSEP分别为0.114 8、0.077 1,RE分别为14.01%、7.81%,显示LWPLS算法建立的模型精度更高。研究表明,采用LWPLS算法可提高安胎丸定量模型的准确性,具有可推广性和广泛的应用性。     

QSAR Study and VolSurf Characterization of Human Intestinal Absorption of Druge

The prediction of human intestinal absorption is a major goal in the design,optimization,and selection of candidates for the develoment of oral drugs.In this study,a computerized method(VolSurf with GRID) was used as a novel tool for predicting human intestinal absorption of test compound,and for determining the critical molecular properties needed for human intestinal absorption.The tested molecules consisted of 20 diverse drug-like compounds.Partial least squares(PLS) discriminant analysis was used to correlate the experimental data with the theoretical molecular properties of human intestinal absorption.A good correlation(r^2=0.95,q^2=0.86) between the molecular modeling results and the experimental data demonstrated that human intestinal absorption could be predicted from the three-dimensional(3D) molecular structure of a compound .Favorable structureal properties identified for the potent intestinal absorption of drugs included strong imbalance between the center of mass of a molecule and the barycentre of its hydrophilic and hydrophobic regions and a definitive hydrophobic region as well as less hydrogen bonding donors and acceptors in the molecule.     

QSAR study and VolSurf characterization of anti-HIV quinolone library

Antiviral quinolones are promising compounds in the search for new therapeutically effective agents for the treatment of AIDS. To rationalize the SAR for this new interesting class of anti-HIV derivatives, we performed a 3D-QSAR study on a library of 101 6-fluoro and 6-desfluoroquinolones, taken either from the literature or synthesized by us. The chemometric procedure involved a fully semiempirical minimization of the molecular structures by the AMSOL program, which takes into account the solvatation effect, and their 3D characterization by the VolSurf/GRID program. The QSAR analysis, based on PCA and PLS methods, shows the key structural features responsible for the antiviral activity.     

Integration of genetic virtual screening patterns and latent multivariate modeling techniques for QSAR optimization based on combinations and/or interactions between peptides and proteins

Both the concept and the model of snug quantitative structure-activity relationship (QSAR) were pro-posed and developed for molecular design through constructing QSAR based on some known mode of receptor/ligand interactions. Many disadvantages of traditional models can be avoided by using the proposed method because the traditional models only determined upon molecular structural features in sample sets themselves. A genetic virtual screening of peptide/protein combinations (GVSPPC) is proposed for the first time by utilizing this idea to examine peptide/protein affinity activities. A genetic algorithm (GA) was developed for screening combinative targets with an interaction mode for virtual receptors. GVSPPC succeeds in disposing difficulties in rational QSAR,in order to search for the ligand/receptor interactions on conditions of unknown structures. Some bioactive oligo-/poly-peptide systems covering 58 angiotensin converting enzyme (ACE) inhibitors and 18 double site mutation residues in camel antibody protein cAb-Lys3 were investigated by GVSPPC with satisfactory results (R 2 cu>0.91,Q 2 cv > 0.86,ERMS=0.19-0.95),respectively,which demonstrates that GVSPPC is more inter-pretable in the ligand-receptor interaction than the traditional QSAR method.     

Liver functional magnetic resonance imaging analysis using a latent variables approach

The liver is a highly vascular organ with a dual blood supply, and it performs a remarkable number of vital functions. Here, we show, through measurement of blood oxygen level‐dependent (BOLD) signal, that liver arterial and hepatic portal blood supplies can be modulated through hyperoxia exposure and by consumption of a standardized meal, respectively. As such, we suggest that hyperoxia modulates the hepatic arterial BOLD signal, whereas a controlled meal changes predominantly the hepatic portal BOLD signal. The hemodynamics of the dual liver blood supplies in response to the aforementioned challenges are complex and variable across subjects, making a general linear model‐based analysis difficult. Therefore, we present the application of two local (at each voxel) hemodynamic response‐independent techniques—principal component analysis and partial least squares—to observe the hypothesized reduction in BOLD contrast during cycles of hyperoxic breathing, when comparing preprandial versus postprandial states in a normally functioning liver. We illustrate the ability of our techniques to differentiate between healthy and diseased livers with an analysis of 17 subjects—11 with normal livers and 6 with liver disease (hepatitis or cirrhosis). Our local analysis can correctly classify all of the subjects. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimization of Fish Quality by Evaluation of Total Volatile Basic Nitrogen (TVB-N) and Texture Profile Analysis (TPA) by Near-Infrared (NIR) Hyperspectral Imaging

Hyperspectral images contain both spectral and spatial image information and were investigated to characterize the freshness of fish. However, most studies of this application have focused on spectral signals rather than image features. The goal of this work was to investigate the ability of spectral and image textural variables for predicting the chemical and physical qualities of fish, respectively, and to optimize the variables for the specific quality determination. The chemical (total volatile basic nitrogen, TVB-N) and physical (texture profile analysis, TPA) properties were investigated. Partial least square (PLS) was applied to develop fish quality prediction models with the spectral and textural variables from the hyperspectral images. The results showed that the TVB-N content of fish fillets was accurately predicted using the spectra. Meanwhile, the TPA parameters were determined through the image textural features with high accuracy, which indicated image textural features were highly related with the TPA parameters. Moreover, spectral and textural features were also extracted from fish eyes and gills and were further used to predict the intact fish quality, taking advantage of the freshness sensitivity of the eyes and gills. The results illustrate that spectra from fish eyes and gills are a potential tool to predict the TVB-N content and TPA parameters for intact fish.     

Averaged and weighted average partial least squares

Two alternative partial least squares (PLS) methods, averaged PLS and weighted average PLS, are proposed and compared with the classical PLS in terms of root mean square error of prediction (RMSEP) for three real data sets. These methods compute the (weighted) average of PLS models with different complexity. The prediction abilities of the alternative methods are comparable to that of the classical PLS but they do not require to determine how many components should be included in the model. They are also more robust in the sense that the quality of prediction depends less on a good choice of the number of components to be included. In addition, weighted average PLS is also compared with the weighted average part of LOCAL, a published method that also applies weighted average PLS, with however an entirely different weighting scheme.     

Rapid Classification and Recognition Method of the Species and Chemotypes of Essential Oils by ATR-FTIR Spectroscopy Coupled with Chemometrics

In the present work, the applicability of attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric tools in recognizing essential oils (EOs) for routine control, was evaluated. EOs belonging to Mentha, Cymbopogon, and Lavandula families and to S. rosmarinus and T. vulgaris species were analyzed, and the performance of several untargeted approaches, based on the synergistic combination of ATR-FTIR and Partial Least Squares Discriminant Analysis (PLS-DA), was tested to classify the species and chemotypes. Different spectra pre-processing methods were employed, and the robustness of the built models was tested by means of a Receiver Operating Characteristic (ROC) curve and random permutations test. The application of these approaches revealed fruitful results in terms of sensitivity and specificity, highlighting the potentiality of ATR-FTIR and chemometrics techniques to be used as a sensitive, cost-effective, and rapid tool to differentiate EO samples according to their species and chemotype.