基于启发式方法和支持向量机方法预测有机物的热导率

构建147个有机物分子结构与其热导率值之间的定量结构-性质关系(QSPR)模型, 探讨影响有机物热导率的结构因素. 以147个化合物作为样本集, 随机选择118个作为训练集, 29个作为测试集. 应用CODESSA软件计算了组成、拓扑、几何、静电和量子化学等描述符, 通过启发式方法(HM)筛选得到5个结构参数并建立线性回归模型; 用所选5个结构参数作为支持向量机(SVM)的输入, 建立非线性的支持向量机回归模型. 预测结果表明: 支持向量机回归模型的性能(复相关系数R²=0.9240)虽略低于启发式回归模型的性能(R²=0.9267), 但是支持向量机方法预测性能(R²=0.9682)高于启发式方法的预测性能(R²=0.9574), 对于QSPR模型来说, 预测性能更重要. 因此, 总体来说支持向量机方法优于启发式方法. 支持向量机方法和启发式方法的提出为工程上提供了一种根据分子结构预测有机物热导率的新方法.     

氨基乙内酰脲类BACE1抑制剂的特征结构与作用机制

对BACE1抑制剂的研究与开发已成为目前治疗阿尔兹海默症的主要研究方向之一。本文选取105个氨基乙内酰脲类BACE1抑制剂作为研究对象,借助比较分子相似性指数(Comparative Molecular Similarity Index, CoMSIA)和分子对接方法,建立定量构效关系预测模型,研究影响化合物抑制活性的特征结构信息,揭示该类抑制剂与靶标之间的作用模式。结果表明,模型(Q_²=0.45, R_²ncv=0.87, R_²pre=0.85)具有较强的预测能力,抑制剂主要占据了靶标的S3、S1和S2"位点,其主要作用力类型为氢键力。实验所得模型和信息可为日后研究开发新型高效的BACE1抑制剂提供一定的理论指导,节省研究时间与费用。     

氧化铁改性蛭石的制备、表征及吸附氟的特性

采用共沉淀法制备了3种不同含铁量的氧化铁改性蛭石(Verm-Fe_x,x=5,10,20),研究了纯蛭石(Verm)和Verm-Fe_x的表面性质及吸附氟的特性。与样品Verm比较,3种Verm-Fe_x中Verm的d₍₀₀₂₎层间距略有升高;Verm-Fe_x的孔体积、表面积、表面分形度均随含铁量的增加而升高,其中微孔体积和外表面积的增加幅度更明显。4种样品的等电点(IEP)也随含铁量的增加而明显升高;初始pH＝5.0时,它们的表面ζ电位分别为-16.4,-6.1,10.5和28.4 mV。4种样品对氟的等温吸附数据用单吸附位Langmuir模型拟合(R²=0.973~0.995)时,Verm的R²最高;双吸附位Langmuir模型可很好地描述3种Verm-Fe_x样品的等温吸附过程(R²=0.991~0.998);Freundlich模型对4种样品吸附数据的拟合度较差(R²=0.835~0.937),但R²随样品含铁量的增加而略微升高。初始pH=5.0时,Verm和Verm-Fe_x(x=5,10,20)对氟的最大吸附容量(q_max)分别为3.18,6.76,9.27和12.43 mg·g^-1。可见,Verm-Fe_x(尤其含铁量较高的产物)对表生环境中氟的吸附固定性能明显高于Verm。     

具有除草活性的吡唑啉并四嗪酮衍生物的设计、合成与定量构效关系的研究

设计合成了一系列未见文献报道的4-乙氧羰基-1,7-二氢-1-取代苯基-5-(未)取代吡唑啉[5,1-d][1,2,3,5]四嗪-7-酮衍生物, 其结构均经过¹H NMR、IR和元素分析表征. 生测结果显示, 与已报道的化合物相比, 它们表现出较好的除草活性. 定量的结构与活性关系研究表明, 它们的除草活性与取代基的立体效应参数和疏水性参数呈现很好的相关性, 相关系数r大于0.8. 当作用对象为油菜时, 化合物的活性可能主要与取代基R¹的摩尔分子折射和取代基R²的疏水性参数有关. 当取代基R¹的摩尔分子折射参数为1.452时, 相应化合物可能具有对油菜最高的除草活性; 当作用对象为稗草时, 化合物的活性主要与取代基R²疏水性参数和Taft (Es)参数有关.     

培氟沙星均三唑硫醚衍生物抗肝癌活性的CoMFA模型与分子设计

基于比较分子力场分析(CoMFA)方法建立24种培氟沙星均三唑硫醚衍生物抗肝癌活性(pM)的三维定量构效关系(3D-QSAR)。训练集中20个化合物用于建立预测模型,测试集10个化合物(含模板分子及新设计的5个分子)作为模型验证。已建立的3D-QSAR模型的交叉验证系数(R_cv²)、非交叉验证系数(R^₂)分别为0.705、0.940,说明所建模型具有较强的稳定性和良好的预测能力。该模型中立体场、静电场贡献率依次为74.8%、25.2%,表明影响抗肝癌活性(pM)的主要因素是取代基的疏水性和空间契合,其次是库仑力、氢键及配位。基于三维等势图,设计了5个具有较高抗肝癌活性的分子,有待医学实验验证。     

基于高维特征非线性筛选的HLA-A*0201限制性CTL表位预测

高活性细胞毒T细胞(CTL)表位鉴定是设计肿瘤疫苗的关键内容.采用天然氨基酸的531个物理化学性质参数表征HLA-A^*0201限制性表位9肽, 从531×9个初始描述子出发, 经二元矩阵重排过滤器粗筛和多轮末尾淘汰精细筛选, 获得18个物理化学意义明确的保留描述子. 18个保留描述子主要涉及除1位、5位外各位置残基的疏水性和空间结构特征, 3位残基疏水性对活性影响最大, 且2位、4位、9位残基共占10个保留描述子,支持2位和9位残基为锚点、3位为关键位点以及4位残基为标志链的现有认知. 对18个保留描述子以支持向量回归构建定量序效模型,其拟合、留一法交叉验证决定系数R²、Q_cv²分别为0.957、0.708; 独立预测决定系数及均方根误差Q_ext² 、RMSE_ext分别为0.818、0.366, 明显优于文献报道. 通过对全组合虚拟9肽的预测, 得到了多条预测活性高于已知表位肽的9肽, 可供实验验证. 较全面阐明了特定位置残基对多肽亲和性的影响规律, 为高活性多肽疫苗分子设计提供了切实指导.     

分子相似性方法用于药物反相色谱定量结构色谱保留关系的研究

 应用分子相似性方法研究药物的反相色谱定量结构 色谱保留关系 (QSRR)。在全面考察药物的分子结构参数的基础上 ,采用分子相似性计算方法 ,将化合物结构信息变量转换为相似系数变量 ,并结合人工神经网络技术 ,对 16 2种药物进行反相色谱定量结构与色谱保留关系的研究。成功地应用分子相似性方法实现了对容量因子这一色谱保留参数的预测 ,建立了物理意义明确、预测能力较强的分子结构参数与反相色谱流动相溶剂强度和容量因子之间的定量关系模型 ,实验验证的结果也比较理想。     

五元杂环并嘧啶类胸苷酸合成酶抑制剂的构效关系和分子对接

用比较分子场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA)研究了38个五元杂环并嘧啶衍生物类胸苷酸合成酶抑制剂的三维定量构效关系(3D-QSAR), 建立了相关预测模型. CoMFA和CoMSIA模型的交互验证相关系数q²分别为0.662和0.672、非交互验证相关系数R²分别为0.921和0.884、外部交互验证相关系数Q_ext²分别为0.85和0.81. 分子对接得到的结合模式与三维定量构效关系得到的结果一致. 结果表明这两种模型都具有良好的预测能力, 可应用于指导化合物的设计和结构修饰, 为进一步设计新型胸苷酸合成酶抑制剂提供了理论依据.     

(1iR, 1iiR, 2iR, 2iiR)-Ni, Nii-(1,3-亚苯基双(亚甲基))环己烷-1,2-二胺作为配体的双核铂配合物的合成及其抗癌活性

以(1ⁱR,1ⁱⁱR,2ⁱR,2ⁱⁱR)-Nⁱ, Nⁱⁱ-(1,3-亚苯基双(亚甲基))环己烷-1,2-二胺(HL)作为配体,设计并合成了7种双核铂配合物,并利用IR,¹H NMR,¹³C NMR,ESI-MS和元素分析等进行了表征。通过MTT法测定目标双核铂配合物对人类HepG-2,A549,HCT-116和MCF-7四种癌细胞系的细胞毒性。结果表明,所有的化合物对HepG-2,A549和HCT-116细胞系均表现了良好的细胞毒活性,但对MCF-7细胞系均无活性。其中,以3-羟基环丁烷-1,1-二羧酸为离去基团的配合物P7对HepG-2和A549细胞系的活性优于卡铂,对HCT-116细胞系的活性接近于奥沙利铂。     

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。     

Liposome Electrokinetic Chromatography: An in vitro Approach for Predicting Ecotoxicity

Liposome electrokinetic chromatography (LEKC) is a powerful tool for the study of drug membrane interactions. An investigation of the use of the retention in LEKC as an in vitro approach to predict the ecotoxicity is proposed. The ecotoxicity parameters (LC₅₀ in fish, daphnia and mysid shrimp, EC₅₀ in green algae and daphnia, and values of chronic ecotoxicity in fish and green algae) for 15 aromatic compounds were extracted from ECOTOX database from the US Environmental Protection Agency. Quantitative retention–activity relationship (QRAR) models for the estimation of ecotoxicity were established. The r ² of the fitted linear equations ranged from 0.80 to 0.87, which suggested good correlation between the retention in LEKC and the ecotoxicity parameters. The predictive ability of the models was evaluated by cross-validation. The results obtained indicated the usefulness of the LEKC systems investigated for the rapid ecotoxicity assessment of aromatic compounds.     

Qualitative and quantitative structure–activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals

In this study, structure–activity relationship (SAR) models have been established for qualitative and quantitative prediction of the blood–brain barrier (BBB) permeability of chemicals. The structural diversity of the chemicals and nonlinear structure in the data were tested. The predictive and generalization ability of the developed SAR models were tested through internal and external validation procedures. In complete data, the QSAR models rendered ternary classification accuracy of >98.15%, while the quantitative SAR models yielded correlation (r²) of >0.926 between the measured and the predicted BBB permeability values with the mean squared error (MSE) <0.045. The proposed models were also applied to an external new in vitro data and yielded classification accuracy of >82.7% and r² > 0.905 (MSE < 0.019). The sensitivity analysis revealed that topological polar surface area (TPSA) has the highest effect in qualitative and quantitative models for predicting the BBB permeability of chemicals. Moreover, these models showed predictive performance superior to those reported earlier in the literature. This demonstrates the appropriateness of the developed SAR models to reliably predict the BBB permeability of new chemicals, which can be used for initial screening of the molecules in the drug development process.     

Quantitative retention-activity relationship models for quinolones using biopartitioning micellar chromatography

A simple and reproducible quantitative retention-activity relationship (QRAR) model utilizing biopartitioning micellar chromatography was developed for the biological parameter estimation of drugs. The correlation between retention factors of quinolones obtained in physiological conditions (pH, ionic strength) and biological activities was investigated using different second-order polynomial models. The predictive and interpretative ability of the chromatographic models was evaluated in terms of cross-validated data (RMSEC, RMSECV and RMSECVi). The aim was to obtain adequate QRAR models of half-life, clearance, volume of distribution, plasma protein combination rate, area under concentration-time curve and toxicity (LD50) of quinolones, and to elucidate the advantages and limitations of using a single parameter as independent variable for describing and estimating the activities.     

Binding conformations,QSAR, and molecular design of Alkene‐3‐quinolinecarbonitriles as Src inhibitors

A theoretical study on binding orientations and quantitative structure–activity relationship (QSAR) of a novel series of alkene‐3‐quinolinecarbonitriles acting as Src inhibitors has been carried out by using the docking study and three‐dimensional QSAR (3D‐QSAR) analyses. The appropriate binding orientations and conformations of these compounds interacting with Src kinase were revealed by the docking studies, and the established 3D‐QSAR models show significant statistical quality and satisfactory predictive ability, with high R² values and q² values: comparative molecular field analysis (CoMFA) model (q² = 0.748, R² = 0.972), comparative molecular similarity indices analysis (CoMSIA) model (q² = 0.731, R² = 0.987). The systemic external validation indicated that both CoMFA and CoMSIA models possessed high predictive powers with $ R{^2}_{\!\!\!\rm pred} $ values of 0.818 and 0.892, $ {r^2}_{\!\!\!\rm m} $ values of 0.879 and 0.886, $ {r^2}_{\!\!\!\rm m(LOO)} $ values of 0.874 and 0.874, $ r^2_{\rm m(overall)} $ values of 0.879 and 0.885, respectively. Several key structural features of the compounds responsible for inhibitory activity were discussed in detail. Based on these structural factors, eight new compounds with quite higher predicted Src‐inhibitory activities have been designed and presented. We hope these theoretical results can offer some valuable references for the pharmaceutical molecular design as well as the action mechanism analysis. © 2012 Wiley Periodicals, Inc.     

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.     

DFT‐based QSAR study and molecular design of AHMA derivatives as potent anticancer agents

A quantitative structure–activity relationship (QSAR) of 3‐(9‐acridinylamino)‐5‐hydroxymethylaniline (AHMA) derivatives and their alkylcarbamates as potent anticancer agents has been studied using density functional theory (DFT), molecular mechanics (MM+), and statistical methods. In the best established QSAR equation, the energy (E_NL) of the next lowest unoccupied molecular orbital (NLUMO) and the net charges (Q_FR) of the first atom of the substituent R, as well as the steric parameter (MR₂) of subsituent R₂ are the main independent factors contributing to the anticancer activity of the compounds. A new scheme determining outliers by “leave‐one‐out” (LOO) cross‐validation coefficient (q) was suggested and successfully used. The fitting correlation coefficient (R²) and the “LOO” cross‐validation coefficient (q²) values for the training set of 25 compounds are 0.881 and 0.829, respectively. The predicted activities of 5 compounds in the test set using this QSAR model are in good agreement with their experimental values, indicating that this model has excellent predictive ability. Based on the established QSAR equation, 10 new compounds with rather high anticancer activity much greater than that of 34 compounds have been designed and await experimental verification. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2007