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.     

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.     

螺环吲哚类MDM2抑制剂的分子对接、定量构效关系和分子动力学模拟

采用分子对接、三维定量构效关系(3D-QSAR)和分子动力学方法研究了21个螺环吲哚类化合物与MDM2蛋白的相互作用, 并建立了相关预测模型. 比较分子场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA)模型的交互验证相关系数q²分别为0.573 和0.651, 非交互验证相关系数r²分别为0.948和0.980. 分子对接得到的结合模式与分子动力学模拟得到的结果一致, 结合模式表明该类螺环吲哚化合物主要通过疏水相互作用和氢键与MDM2结合. 基于上述相互作用模型设计并合成了6个新结构螺环吲哚化合物, 并在MDM2高表达的前列腺癌LNCaP细胞株上测定其活性, 结果表明化合物5, 6的半数抑制浓度均低于1μg·mL^-1, 可作为新的抗肿瘤药物先导化合物进一步深入研究. 本研究对以MDM2为靶点的新结构螺环吲哚类抑制剂的开发提供了理论和实验依据.     

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.     

糖原合成激酶-3β马来酰胺类抑制剂构效关系的理论研究

建立糖原合成激酶-3β(GSK-3β)抑制剂的三维构效关系,可预测新的糖原合成激酶-3β抑制剂.通过确定分子的药效构象,与选定的模板分子进行叠合,采用比较分子力场分析法(Co MFA)和比较分子相似性指数分析法(Co MSIA)分别建立38个糖原合成激酶-3β抑制剂的3D-QSAR模型.比较分子力场分析法所建立的模型的决定系数q2=0.711,非交叉验证系数r2=0.887,标准偏差ES=0.411,显著系数F=109.856;比较分子相似性指数分析法所建立模型的决定系数q2=0.605,非交叉验证系数r2=0.931,标准偏差ES=0.326,显著系数F=122.122.该模型在一定程度上反映了结合部位的性质要求,解释马来酰胺类抑制剂的构效关系,具有较好的预测能力.     

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。     

GPR40激动剂作用方式及药效团模型研究

为阐明GPR40与其激动剂分子之间的相互作用方式,构建可用于GPR40激动剂分子先导化合物筛选的药效团模型。我们利用分子对接技术将GPR40与其激动剂分子进行对接,分析分子与受体之间相互作用的关键氨基酸和结合方式,采用药团模型法分别构建了基于受体-配体复合物(CBP)和基于激动剂分子共同特征(HipHop)的药效团模型,HipHop模型采用测试集法进行验证。结果显示GPR40与小分子相互作用的关键氨基酸主要有ARG183、TYR91、TYR2240、ARG2258、PHE142等,相互作用方式则主要为氢键、盐桥、Pi-Pi Stacking以及疏水作用,以药团模型法构建了10个HipHop模型,其中8号药效团为最优模型,可用于GPR40激动剂分子的虚拟筛选研究,这为GPR40激动剂药物分子设计奠定了理论基础。     

马来酰胺类糖原合成酶激酶-3β抑制剂的分子对接和三维定量构效关系

通过分子对接和三维定量构效关系(3D-QSAR)两种方法来确定两类马来酰胺类的糖原合成酶激酶-3β(GSK-3β)抑制剂的结合方式. 首先, 用分子对接确定抑制剂与GSK-3β结合模式及其相互作用; 然后用比较分子力场分析法(CoMFA)与比较分子相似性指数分析法(CoMSIA)对48个化合物做三维定量构效关系的分析. 两种方法得出的交互验证回归系数分别为0.669(CoMFA)和0.683(CoMSIA), 证明该模型具有很好的统计相关性, 同时也说明该模型具有较高的预测能力.根据该模型提供的信息, 设计出9个预测活性较好的分子.     

马来酰胺类糖原合成酶激酶-3β抑制剂的分子对接和三维定量构效关系

通过分子对接和三维定量构效关系(3D-QSAR)两种方法来确定两类马来酰胺类的糖原合成酶激酶-3β(GSK-3β)抑制剂的结合方式.首先,用分子对接确定抑制剂与GSK-3β的结合模式及其相互作用;然后用比较分子力场分析法(CoMFA)与比较分子相似性指数分析法(CoMSIA)对48个化合物做三维定量构效关系的分析.两种方法得出的交互验证回归系数分别为0.669(CoMFA)和0.683(CoMSIA),证明该模型具有很好的统计相关性,同时也说明该模型具有较高的预测能力.根据该模型提供的信息,设计出9个预测性较好的分子.     

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.     

QSAR models of cytochrome P450 enzyme 1A2 inhibitors using CoMFA,CoMSIA and HQSAR

Quantitative structure–activity relationship (QSAR) studies were conducted on an in-house database of cytochrome P450 enzyme 1A2 inhibitors using the comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA) and hologram QSAR (HQSAR) approaches. The database consisted of 36 active molecules featuring varied core structures. The model based on the naphthalene substructure alignment incorporating 19 molecules yielded the best model with a CoMFA cross validation value q² of 0.667 and a Pearson correlation coefficient r² of 0.976; a CoMSIA q² value of 0.616 and r² value of 0.985; and a HQSAR q² value of 0.652 and r² value of 0.917. A second model incorporating 34 molecules aligned using the benzene substructure yielded an acceptable CoMFA model with q² value of 0.5 and r² value of 0.991. Depending on the core structure of the molecule under consideration, new CYP1A2 inhibitors will be designed based on the results from these models.     

Three-dimensional quantitative structure-activity relationship of 4,5,6,7-tetrahydrothienopyridines analogues as glucose-6-phosphatase inhibitors

A Three-Dimensional Quantitative Structure-activity Relationship (3D-QSAR) model that correlates the biological activities with the chemical structures of a series of Glucose-6-phosphatase inhibitors, exemplified by the 4,5,6,7-tetrahydrothienopyridines derivatives, was established by means of comparative molecular field analysis (CoMFA). The resulting leave-one-out cross-validated value (q²=0.600) and non-cross-validated value (r²=0.956) indicate that the obtained pharmacophore model indeed mimics the steric and electrostatic environment, where inhibitors bind to the enzyme. Furthermore, the developed model also possesses promising predictive ability as discerned by the testing on the external test set. The analysis of the CoMFA contour map, which reveal how steric and electrostatic interactions contribute to inhibitors' bioactivities, provide us with the important information to understand the molecular nature of inhibitor-enzyme interactions and to aid in the design of more potent Glucose-6-phosphatase inhibitors.     

Molecular dynamics and pharmacophore modelling studies of different subtype (ALK and EGFR (T790M)) inhibitors in NSCLC

Extensively validated 3D pharmacophore models for ALK (anaplastic lymphoma kinase) and EGFR (T790M) (epithelial growth factor receptor with acquired secondary mutation) were developed. The pharmacophore model for ALK (r² = 0.96, q² = 0.692) suggested that two hydrogen bond acceptors and three hydrophobic groups arranged in 3-D space are essential for the binding affinity of ALK inhibitors. Similarly, the pharmacophore model for EGFR (T790M) (r² = 0.92, q² = 0.72) suggested that the presence of a hydrogen bond acceptor, two hydrogen bond donors and a hydrophobic group plays vital role in binding of an inhibitor of EGFR (T790M). These pharmacophore models allowed searches for novel ALK and EGFR (T790M) dual inhibitors from multiconformer 3D databases (Asinex, Chembridge and Maybridge). Finally, the eight best hits were selected for molecular dynamics simulation, to study the stability of their complexes with both proteins and final binding orientations of these molecules. After molecular dynamics simulations, one hit has been predicted to possess good binding affinity for both ALK and EGFR (T790M), which can be further investigated for its experimental in-vitro/in-vivo activities.     

Strategies to design pyrazolyl urea derivatives for p38 kinase inhibition: a molecular modeling study

The p38 protein kinase is a serine–threonine mitogen activated protein kinase, which plays an important role in inflammation and arthritis. A combined study of 3D-QSAR and molecular docking has been undertaken to explore the structural insights of pyrazolyl urea p38 kinase inhibitors. The 3D-QSAR studies involved comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA). The best CoMFA model was derived from the atom fit alignment with a cross-validated r ² (q ²) value of 0.516 and conventional r ² of 0.950, while the best CoMSIA model yielded a q ² of 0.455 and r ² of 0.979 (39 molecules in training set, 9 molecules in test set). The CoMFA and CoMSIA contour maps generated from these models provided inklings about the influence of interactive molecular fields in the space on the activity. GOLD, Sybyl (FlexX) and AutoDock docking protocols were exercised to explore the protein–inhibitor interactions. The integration of 3D-QSAR and molecular docking has proffered essential structural features of pyrazolyl urea inhibitors and also strategies to design new potent analogues with enhanced activity.     

Identification of potential influenza virus endonuclease inhibitors through virtual screening based on the 3D-QSAR model

Influenza endonucleases have appeared as an attractive target of antiviral therapy for influenza infection. With the purpose of designing a novel antiviral agent with enhanced biological activities against influenza endonuclease, a three-dimensional quantitative structure-activity relationships (3D-QSAR) model was generated based on 34 influenza endonuclease inhibitors. The comparative molecular similarity index analysis (CoMSIA) with a steric, electrostatic and hydrophobic (SEH) model showed the best correlative and predictive capability (q ² = 0.763, r ² = 0.969 and F = 174.785), which provided a pharmacophore composed of the electronegative moiety as well as the bulky hydrophobic group. The CoMSIA model was used as a pharmacophore query in the UNITY search of the ChemDiv compound library to give virtual active compounds. The 3D-QSAR model was then used to predict the activity of the selected compounds, which identified three compounds as the most likely inhibitor candidates.     

Design,Synthesis, Biological Evaluation,and Molecular Modeling of 2-Difluoromethylbenzimidazole Derivatives as Potential PI3Kα Inhibitors

PI3Kα is one of the potential targets for novel anticancer drugs. In this study, a series of 2-difluoromethylbenzimidazole derivatives were studied based on the combination of molecular modeling techniques 3D-QSAR, molecular docking, and molecular dynamics. The results showed that the best comparative molecular field analysis (CoMFA) model had q² = 0.797 and r² = 0.996 and the best comparative molecular similarity indices analysis (CoMSIA) model had q² = 0.567 and r² = 0.960. It was indicated that these 3D-QSAR models have good verification and excellent prediction capabilities. The binding mode of the compound 29 and 4YKN was explored using molecular docking and a molecular dynamics simulation. Ultimately, five new PI3Kα inhibitors were designed and screened by these models. Then, two of them (86, 87) were selected to be synthesized and biologically evaluated, with a satisfying result (22.8 nM for 86 and 33.6 nM for 87).     

Comparative QSAR studies using HQSAR,CoMFA, and CoMSIA methods on cyclic sulfone hydroxyethylamines as BACE1 inhibitors

The inhibition of β-secretase (BACE1) is currently the main pharmacological strategy available for Alzheimer’s disease (AD). 2D QSAR and 3D QSAR analysis on some cyclic sulfone hydroxyethylamines inhibitors against β-secretase (IC₅₀: 0.002–2.75 μM) were carried out using hologram QSAR (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) methods. The best model based on the training set was generated with a HQSAR q² value of 0.693 and r² value of 0.981; a CoMFA q² value of 0.534 and r² value of 0.913; and a CoMSIA q² value of 0.512 and r² value of 0.973. In order to gain further understand of the vital interactions between cyclic sulfone hydroxyethylamines and the protease, the analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the BACE1. The final QSAR models could be helpful in the design and development of novel active BACE1 inhibitors.     

Studies of febuxostat analogues as xanthine oxidase inhibitors through 3D-QSAR,Topomer CoMFA and molecular modeling

Xanthine oxidase, a complex molybdoflavoprotein, catalyzes the hydroxylation of xanthine to uric acid, which has emerged as an important target for gout and hyperuricemia. In this work, a combination of molecular modeling methods was performed on a series of febuxostat analogues as xanthine oxidase inhibitors to establish molecular models for new drug design, including three-dimensional quantitative structure–activity relationship, topomer comparative molecular field analysis (CoMFA), molecular docking and molecular dynamic simulations. The optimal CoMFA model yielded a leave-one-out correlation coefficient (q²) of 0.841 and a non-validated correlation coefficient (r²) of 0.985. The respective q² and r² of the best comparative molecular similarity indices analysis (CoMSIA) model were 0.794 and 0.972, respectively. The Topomer CoMFA model provided a q² of 0.915 and an r² of 0.977. 3D contour maps generated from CoMFA and CoMSIA have identified several key features responsible for the inhibition activity. Molecular modeling was taken to further elucidate the proposed binding conformations of the inhibitors to the protein. The obtained results can be served as a useful guideline for designing novel febuxostat derivatives with improved activity against xanthine oxidase.

     

Computational studies on N-phenyl pyrrole derivatives as MmpL3 inhibitors in Mycobacterium tuberculosis

The fight against tuberculosis (TB) is a time immemorial one and the emergence of new drug resistant strains of Mycobacterium tuberculosis keeps throwing new challenges to the scientific community immersed in finding mechanisms to control this dreaded disease. Computer aided drug designing (CADD) is one of the several approaches that can assist in identifying the potent actives against Mycobacterium. In this work, a series of 109 known Mycobacterial membrane proteins large 3 (MmpL3) inhibitors were pooled and atom based 3D QSAR analysis was performed to understand the structural features essential for inhibitory activity against the MmpL3, known to be a key player in transporting substances critical for cell wall integrity of Mycobacterium. The data set employed was randomly split into training set and test set molecules. The training set of 74 molecules was used to derive CoMFA and CoMSIA models that were statistically reliable (CoMFA: q²_loo = 0.53; r²_ncv = 0.93 and CoMSIA: q²_loo = 0.60; r²_ncv = 0.93). The derived models also exhibited good external predictive ability (CoMFA: r²_pred = 0.78 and CoMSIA: r²_pred = 0.79). The results are quite encouraging and information derived from these analyses was applied to design new molecules. The designed molecule showed appreciable predicted activity values and reasonably good ADMET profile. The strategy used in designing new molecules can be pursued in the hunt for new chemical entities targeting MmpL3, expanding the existing arsenal against TB.