毒蕈碱受体激动剂的三维定量构效关系研究

采用比较分子场分析法(CoMFA)研究了55个四氢吡啶类毒蕈碱受体激动剂的三维定量构效关系(3D-QSAR), 建立了具有较强预测能力的3D-QSAR模型. 所得模型的交叉验证相关系数(q²)为0.507, 常规相关系数(R²)为0.982 , 标准方差为0.218, 说明系列化合物分子周围立体场和静电场的分布与生物活性间存在良好的相关性. 模型不仅很好地预测了训练集和测试集化合物的活性, 而且为设计活性更高的受体激动剂提供了理论依据.     

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.     

N,N-二甲基-2-溴苯乙胺类衍生物对大鼠生物活性的三维构效关系研究

N,N-二甲基-2-溴苯乙胺类衍生物是一类有效的肾上腺素阻断剂, 具有较高的生物活性. 通过比较分子力场分析(CoMFA)方法, 建立了22个标题化合物对大鼠阻断活性的三维定量结构-活性关系(3D-QSAR)模型. 该模型显示立体场、静电场对生物活性贡献分别为67.3%, 32.7%, 其中立体场与受体之间的相互作用是造成阻断剂生物活性差别的主要因素. 此3D-QSAR模型的交叉验证系数q²＝0.862, 传统的相关系数(非交互验证系数)R²＝0.962. 该模型给出的预测值与实验值非常接近, 其方差比F＝503.7, 估计标准误差(S)为0.11. 根据CoMFA模型的立体场、静电场三维等值线图不仅直观地解释了结构与活性的关系, 而且为进一步设计高活性的标题化合物提供一定的理论依据.     

2-嘧啶氧基-N-芳基苄胺类化合物的ALS抑制活性的QSAR研究

乙酰乳酸合成酶(ALS)或乙酰羟酸合成酶(AHAS)存在于植物的生长过程中, 很多此类酶的抑制剂实际上作为除草剂被广泛用于农业生产中. 生物活性测试结果表明, 2-嘧啶氧基-N-芳基苄胺类化合物对ALS具有一定的抑制活性. 在此基础上, 我们用两种三维定量构效关系(3D-QSAR)研究方法: 比较分子立场分析(CoMFA)和比较分子相似性指数分析(CoMSIA), 对该类化合物进行了3D-QSAR研究, 并建立了相关的预测模型. 其中, CoMFA模型的交叉验证相关系数(r_cv²)为0.801, 非交叉验证相关系数(r²)为0.947, 标准偏差(s)为0.136, F值为133.371. CoMSIA模型的r_cv²为0.744, r²为0.883, s为0.202, F值为56.472. 计算结果表明, 2-嘧啶氧基-N-芳基苄胺类化合物与ALS抑制活性有一定的相关性. 获得的CoMFA和CoMSIA模型, 将应用于指导该类化合物的设计.     

硫色烯并噻唑胺类衍生物对乙酰胆碱酶抑制活性的QSAR模型*

基于化学拓扑理论,计算20 种硫色烯并噻唑胺类衍生物分子的电性距离矢量指数(m_t)。经最佳变量子集回归方法建立上述分子对电鳗乙酰胆碱酶体外抑制活性(pM)与m_t的最佳三元QSAR模型,其判定系数(R₂)和逐一剔除法交叉验证系数R_cv2依次为0.936和0.850。通过R₂、R_adj2、F、R_cv2、V_IF、A_IC、F_IT等检验,该模型具有令人满意的稳健性和预测能力。依据模型建议硫色烯并噻唑胺类衍生物对电鳗乙酰胆碱酶体外抑制的可能机理：分子疏水性起到主要及正向作用,氢键则起次要且为负向作用。     

靛玉红类CDK1抑制剂的同源模建、分子对接及3D-QSAR研究

细胞周期蛋白依赖性激酶1的异常表达会导致G2期的停滞及多种肿瘤的发生,故CDK1近年来已成为一个理想的治疗靶点. 本文以细胞分裂调控蛋白2的同源体为模板,同源模建了CDK1的结构,并与靛玉红类小分子抑制剂进行分子对接. 分别运用三种叠合方法进行分子叠合,并在此基础上采用Sybyl 7.1中的比较分子场分析(CoMFA)模块及Discovery Studio 3.0中的三维定量构效关系(3D-QSAR)模块(以下简称为DS)分别建立了3D-QSAR模型. 其中,将分子对接叠合与公共骨架叠合联合运用的叠合方法所得3D-QSAR模型的评价参数是最佳的(CoMFA：q²=0.681,r²=0.909,r_pred.²=0.836; DS：q²=0.579,r²=0.971,r_pred.²=0.795,其中q²为交叉验证系数,r²为非交叉验证系数). 本文的研究结果在对靛玉红类小分子进行结构修饰设计出新的CDK1抑制剂方面,可提供重要的理论基础.     

吡啶并嘧啶衍生物抗乳腺癌活性的CoMFA模型

基于比较分子力场分析(CoMFA)方法建立25种吡啶并嘧啶衍生物抗乳腺癌活性(pIC)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6个化合物(含模板分子)作为模型验证。已建立的CoMFA模型的交叉验证系数(R_(cv)~2)、非交叉验证系数(R~2)分别为0.467、0.891,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、静电场对pIC的贡献率依次为40.8%、59.2%。基于此研究结果,设计了4个具有较高抗乳腺癌活性的新化合物,有待医学实验验证。     

取代嘧啶衍生物抗前列腺癌活性的3D-QSAR研究

运用比较分子力场分析(CoMFA)方法,建立18种取代嘧啶衍生物抗前列腺癌活性(pM)的三维定量构效关系。训练集中15个化合物用于建立预测模型,测试集13个化合物(含10号模板分子和新设计的9个分子)作为模型验证。建立的CoMFA模型的交叉验证系数(R_ev²)、非交叉验证系数(R²)分别为0.344、0.935,说明所建模型具有较强的鲁棒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71.6%、28.6%。影响取代嘧啶衍生物抗前列腺癌活性的主要因素是取代基的疏水作用和空间位阻,其次是取代基的库仑力、氢键及配位作用。基于此研究结果,设计了9个新化合物,其抗前列腺癌活性有待医学实验验证。     

p,p′-芳炔桥连的四联萘拓扑环芳分子的设计与合成

基于联萘衍生物手性构型高度稳定的特点, 以光学活性的(R)-2,2'-二乙炔基-1,1'-联萘为模板, 设计了3个有趣的拓扑环芳分子——含有4个手性联萘单元的(R,R,R,R)-2a～2c, 并探讨了它们的合成. 合成路线涉及三甲基硅(Me₃Si-)保护基的控制导入, 对位取代的芳基连接桥的链接, 保护基的脱去以及分子间偶合成环4个步骤. 用比旋光度([α]_D), MS, IR, UV-Vis, ¹H和¹³C NMR以及元素分析表征了这些化合物.     

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。     

3D-QSAR study of hallucinogenic phenylalkylamines by using CoMFA approach

The three-dimensional quantitative structure–activity relationship (3D-QSAR) has been studied on 90 hallucinogenic phenylalkylamines by the comparative molecular field analysis (CoMFA). Two conformations were compared during the modeling. Conformation I referred to the amino group close to ring position 6 and conformation II related to the amino group trans to the phenyl ring. Satisfactory results were obtained by using both conformations. There were still differences between the two models. The model based on conformation I got better statistical results than the one about conformation II. And this may suggest that conformation I be preponderant when the hallucinogenic phenylalkylamines interact with the receptor. To further confirm the predictive capability of the CoMFA model, 18 compounds with conformation I were randomly selected as a test set and the remaining ones as training set. The best CoMFA model based on the training set had a cross-validation coefficient q ² of 0.549 at five components and non cross-validation coefficient R ² of 0.835, the standard error of estimation was 0.219. The model showed good predictive ability in the external test with a coefficient R _pre² of 0.611. The CoMFA coefficient contour maps suggested that both steric and electrostatic interactions play an important role. The contributions from the steric and electrostatic fields were 0.450 and 0.550, respectively.     

Design of new CD38 inhibitors based on CoMFA modelling and molecular docking analysis of 4‑amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides

In this study, based on molecular docking analysis and comparative molecular field analysis (CoMFA) modelling of a series of 71 CD38 inhibitors including 4?amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides, new CD38 inhibitors were designed. The interactions of the molecules with the greatest and the lowest activities with the nicotinamide mononucleotide (NMN) binding site were investigated by molecular docking analysis. A CoMFA model with four partial least squares regression (PLSR) components was developed to predict the CD38 inhibitory activity of the molecules. The r² values for the training and test sets were 0.89 and 0.82, respectively. The Q² values for leave-one-out cross-validation (LOO-CV) and leave-many-out cross-validation (LMO-CV) tests on the training set were 0.65 and 0.64, respectively. The CoMFA model was validated by calculating several statistical parameters. CoMFA contour maps were interpreted, and structural features that influence the CD38 inhibitory activity of molecules were determined. Finally, seven new CD38 inhibitors with greater activity with respect to the greatest active molecules were designed.     

Pharmacophore interactions analysis and prediction of inhibitory activity of 1,7-diazacarbazoles as checkpoint kinase 1 inhibitors: application of molecular docking, 3D-QSAR and RBF neural network

In the present study, we mainly focused on new synthesized 1,7-diazacarbazole derivatives (44 active molecules) as Chk1 inhibitors to build 3D-QSAR model. Comparative molecular field analysis (CoMFA) model with three principal components was developed. The relative contributions in building of CoMFA model were 64.41 % for steric field and 35.59 % for electrostatic field. R ² values for training and test sets of CoMFA model were 0.8724 and 0.7818, respectively, and squared correlation coefficient for leave-one-out cross-validation test (q ²) was 0.6753. To improve the predictive power, a new 3D-QSAR model was developed by using radial basis function network (RBFN) and score of CoMFA interactions energy values as input variables. Scores 1, 2 and 3 were used as input variables, and a RBFN model with seven centers and spread value equal to 95 was developed to create a nonlinear 3D-QSAR model. R ² values for training and test sets were 0.9613 and 0.8564, and q ² for leave-one-out cross-validation test was 0.9258. Docking of all molecules to 3DX ligand binding site of Chk1 receptor indicated six interactions as pharmacological interactions between compounds and binding site of receptors. These pharmacological interactions were hydrogen bonding with LEU-15 and GLU-85 in main chain and four van der Waals interactions with LEU-15, VAL-23, TYR-86 and LEU-137 in side chain. CoMFA contour plots were used to design new inhibitors, and inhibitory activity of each compound was predicted by using CoMFA and RBFN models.     

Triple-layered QSAR studies on substituted 1,2,4-trioxanes as potential antimalarial agents: superiority of the quantitative pharmacophore-based alignment over common substructure-based alignment

This study reports the utilization of three approaches – pharmacophore, CoMFA/CoMSIA and HQSAR studies – to identify the essential structural requirements in 3D chemical space for the modulation of the antimalarial activity of substituted 1,2,4-trioxanes. The superiority of quantitative pharmacophore-based alignment (QuantitativePBA) over global minima energy conformer-based alignment (GMCBA) has been reported in CoMFA and CoMSIA studies. The developed models showed good statistical significance in internal validation (q ², group cross-validation and bootstrapping) and performed very well in predicting the antimalarial activity of test set compounds. Structural features in terms of their steric, electrostatic and hydrophobic interactions in 3D space have been found to be important for the antimalarial activity of substituted 1,2,4-trioxanes. Further, the HQSAR studies based on the same training and test set acted as an additional tool to find the sub-structural fingerprints of substituted 1,2,4-trioxanes for their antimalarial activity. Together, these studies may facilitate the design and discovery of new substituted 1,2,4-trioxanes with potent antimalarial activity.     

Comparison of Estrogen Receptor α and β Subtypes Based on Comparative Molecular Field Analysis (CoMFA)

Abstract

A substantial body of evidence indicates that both humans and wildlife suffer adverse health effects from exposure to environmental chemicals that are capable of interacting with the endocrine system. The recent cloning of the estrogen receptor β subtype (ER-β) suggests that the selective effects of estrogenic compounds may arise in part by the control of different subsets of estrogen-responsive promoters by the two ER subtypes, ER-α and ER-β. In order to identify the structural prerequisites for ligand-ER binding and to discriminate ER-α and ER-3 in terms of their ligand-binding specificities, Comparative Molecular Field Analysis (CoMFA) was employed to construct a three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) model on a data set of 31 structurally-diverse compounds for which competitive binding affinities have been measured against both ER-α and ER-β. Structural alignment of the molecules in CoMFA was achieved by maximizing overlap of their steric and electrostatic fields using the Steric and Electrostatic ALignment (SEAL) algorithm. The final CoMFA models, generated by correlating the calculated 3D steric and electrostatic fields with the experimentally observed binding affinities using partial least-squares (PLS) regression, exhibited excellent self-consistency (r ² > 0.99) as well as high internal predictive ability (q ² > 0.65) based on cross-validation. CoMFA-predicted values of RBA for a test set of compounds outside of the training set were consistent with experimental observations. These CoMFA models can serve as guides for the rational design of ER ligands that possess preferential binding affinities for either ER-α or ER-β. These models can also prove useful in risk assessment programs to identify real or suspected EDCs.     

Pharmacophore Screening on Piperidinecarboxamides Derivatives Based on GALAHAD and CoMFA Models

In order to discover the novel anticonvulsant drugs, pharmacophore screening of the anticonvulsant inhibitors was enforced. Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets (GALAHAD) and Comparative Molecular Field Analysis (CoMFA) studies were combined to implement our research. Firstly, multiple models were generated using GALAHAG based on high active molecules. Secondly, several of them were validated using the CoMFA study. Finally, a good values of q² from training set and promising predictive power from test set were obtained based on one model simutaneously. One model had been selected as the most reasonable pharmacophore model. The results of the CoMFA study based on the model 1 suggested that both steric and electrostatic interactions played important roles.     

Docking-based 3D-QSAR study of pyridyl aminothiazole derivatives as checkpoint kinase 1 inhibitors

Checkpoint kinase 1 (Chk1) is a promising target for the design of novel anticancer agents. In the present work, molecular docking simulations and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on pyridyl aminothiazole derivatives as Chk1 inhibitors. AutoDock was used to determine the probable binding conformations of all the compounds inside the active site of Chk1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed based on the docking conformations and alignments. The CoMFA model produced statistically significant results with a cross-validated correlation coefficient (q²) of 0.608 and a coefficient of determination (r²) of 0.972. The reliable CoMSIA model with q² of 0.662 and r² of 0.970 was obtained from the combination of steric, electrostatic and hydrogen bond acceptor fields. The predictive power of the models were assessed using an external test set of 14 compounds and showed reasonable external predictabilities (r²_pred) of 0.668 and 0.641 for CoMFA and CoMSIA models, respectively. The models were further evaluated by leave-ten-out cross-validation, bootstrapping and progressive scrambling analyses. The study provides valuable information about the key structural elements that are required in the rational design of potential drug candidates of this class of Chk1 inhibitors.     

Comparative molecular field analysis of chromatographic hydrophobicity indices for some coumarin analogs

The chromatographic hydrophobicity index (CHI) is an HPLC‐based parameter that provides reliable guidance in optimization of pharmacological efficiency and adsorption, distribution, metabolism and exertion (ADME) profile of drug candidates. In the present work, classical and three‐dimensional quantitative structure–property relationship (QSPR) models were developed for prediction of CHI values of some 4‐hydroxycoumarin analogs on immobilized artificial membrane column. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) as 3D–QSPR methods were performed to gain insight into the key structural factors affecting on the chromatographic hydrophobicity of interested chemicals. The calculated parameters of Q ², R ² and standard error were 0.545, 0.996 and 0.773 for CoMFA model and 0.815, 0.986 and 1.44 for CoMSIA model, respectively. The contour maps for steric fields of the CoMFA model illustrate that the hydrophobicity of chemicals will be higher when the positions of R6, R7 and R8 in the 4‐hydroxycuomarin ring are substituted by alkyl groups. Moreover, by the analysis of the plots of electrostatic fields, it was concluded that the CHI value greatly increases if one hydrogen on coumarin ring is substituted by the F, Cl, Br, OH or OCH₃ group.     

High-affinity interactions of ligands at recombinant Guinea pig 5HT7 receptors

The serotonin 5HT₇ receptor has been implicated in numerous physiological and pathological processes from circadian rhythms [1] to depression and schizophrenia. Clonal cell lines heterologously expressing recombinant receptors offer good models for understanding drug-receptor interactions and development of quantitative structure-activity relationships (QSAR). Comparative Molecular Field Analysis (CoMFA) is an important modern QSAR procedure that relates the steric and electrostatic fields of a set of aligned compounds to affinity. Here, we utilized CoMFA to predict affinity for a number of high-affinity ligands at the recombinant guinea pig 5HT₇ receptor. Using R-lisuride as the template, a final CoMFA model was derived using procedures similar to those of our recent papers [2, 3, 4] The final cross-validated model accounted for >85% of the variance in the compound affinity data, while the final non-cross validated model accounted for >99% of the variance. Model evaluation was done using cross-validation methods with groups of 5 ligands. Twenty cross-validation runs yielded an average predictive r²(q²) of 0.779 ± 0.015 (range: 0.669–0.867). Furthermore, 3D-chemical database search queries derived from the model yielded hit lists of promising agents with high structural similarity to the template. Together, these results suggest a possible basis for high-affinity drug action at 5HT₇ receptors.