环氧合酶－2抑制剂的三维定量构效关系研究

建立三环系COX－1和COX－2抑制剂结构与活性的三维定量构效关系模型，为设 计新型的具有选择性的COX－2抑制剂提供线索。通过与酶的对接并优化，确定化合 物在受体结合腔中的构象，利用比较分子力场分析方法建立三维定量构效关系模型 。模型1R_(cv)~2=0.685，最佳主成分数为6，传统相关系数为R~2=0.988, F-726. 2，标准偏差S = 0.080；模型2 R_(cv)~2 = 0.573，最佳主成分数为6，传统相关 系数为R~2=0.996, F = 1147.6，标准偏差S = 0.034。所得的模型不仅解释了化合 物的构效关系，而且对预测集中的化合物有很好的预测能力；比较不同模型的系数 相关图，分析了结构与活性、结构与选择性的关系，得到的结果可以指导新化合物 的设计与合成。     

查耳酮类似物的抗结核分支杆菌3D-QSAR研究

采用三维原子场全息作用矢量(3D-HoVAIF)描述子对25种查耳酮类似物的化学结构与抗结核分支杆菌活性进行定量构效关系研究,建立了SMR-PLS定量构效关系模型,得到了较高的复相关系数(R2=0.777)和交互检验复相关系数(Q2=0.634).并对ClogP进行建模,效果明显,R2=0.853,Q2=0.755,说明模型具有良好的稳定性和预测能力,证明了该三维原子场全息作用矢量在分子结构表征和生物活性预测上的适用性,值得进一步推广应用.     

新型苯环5-(取代)苯甲酰胺基苯磺酰脲类化合物的比较定量构效关系研究

采用比较分子力场分析(CoMFA)方法, 对26个新型苯环5-(取代)苯甲酰胺基苯磺酰脲类化合物的除草活性进行了三维定量构效关系(3D-QSAR)研究, 建立了三维定量构效关系CoMFA模型(R²=0.948, F=91.364, SE=0.141). 结果表明, 此类磺酰脲类化合物的除草活性与苯环5位取代基的立体结构和电场性质密切相关. 根据CoMFA模型的立体场和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目标化合物提供理论依据.     

甲状腺激素受体配体化合物的定量构效关系(QSAR)研究

研究了68个TR（Thyroid Hormone Receptor,甲状腺激素受体）配体化合物的化学结构与活性的定量构效关系．采用实验室新近提出的三维原子场全息相互作用矢量,对化合物进行了结构参数化表达,采用逐步回归对变量进行筛选后,建立了定量构效关系模型．复相关系数和交互检验复相关系数R^2=0．767,Q^2=0．625（TRα）,R^2=0．734,Q^2=0．61（TRβ）．模型具有良好的稳定性和预测能力,证明了该三维原子场全息相互作用矢量在分子结构表征和生物活性预测上的适用性,并可应用于潜在和新型的TR配体化合物的设计和开发．     

Combretastatins类微管蛋白抑制剂的定量构效关系与结合模式

以Combretastatins的B环改造化合物为研究对象, 采用遗传函数分析方法进行了二维定量构效关系研究. 研究结果表明, Apol, PMI-mag, Dipole-mag, Hbond donor和RadOfGyration等描述符对该系列抑制剂活性的贡献最大. 采用比较分子场分析方法(CoMFA)和比较分子相似因子分析方法(CoMSIA)进行了三维定量构效关系研究, 建立的CoMFA和CoMSIA模型的交叉验证相关系数q2分别为0.630和0.634, 具有较强的预测能力. 利用CoMFA和CoMSIA模型的三维等势图解析了Combretastatins类化合物的构效关系, 阐明了B环上各取代基对抑制微管蛋白聚合活性的影响, 同时应用分子对接方法分析并验证了定量构效关系模型.     

一类新型N型钙通道阻滞剂的比较定量构效关系研究

使用结构和量子化学参数对一系列N取代N-甲基二肽衍生物的N型钙通道阻滞活性进行了定量构效关系研究, 并使用SOMFA方法建立了三维定量构效关系模型. 结果表明分子的范德华体积和最低未占据轨道能量是影响化合物生物活性的主要因素. N原子上取代基的溶剂可及面积也对化合物的钙通道阻滞活性有重要影响. 三维定量构效关系模型进一步支持了以上结果. 这些研究结果可为设计更高活性的N型钙通道阻滞剂提供有价值的参考信息.     

吡咯烷类胞液型磷脂酶A2抑制剂的比较分子力场分析

胞液型磷脂酶A2能引发关节炎，针对胞液型磷脂酶A2的抑制剂有可能成为治疗关节炎的特效药，因此引起了广泛的关注.文章对于吡咯烷类胞液型磷脂酶A2抑制剂进行了三维定量构效关系研究，利用比较分子力场分析构建了该类分子的定量构效关系，得到三维等值线图，为胞液型磷脂酶A2抑制剂的进一步改造提供了有益的启示.     

酪氨酸激酶抑制剂的三维定量构效关系研究

利用柔性原子受体模型(FLARM)方法对一系列的异黄酮和喹诺酮衍生物表皮生长因子受体酪氨酸激酶抑制剂进行了三维定量构效关系研究，得到了合理的构效关系模型.FLARM方法的计算结果还给出了虚拟的受体模型，该模型说明了抑制剂与受体之间可能的相互作用.由该虚拟受体模型得到的受体-配体相互作用与Novartis药效团模型比较类似.     

腺苷激酶抑制剂的模拟设计

实验科学工作者们一直致力于寻找新的腺苷激酶抑制剂。这里我们分别从蛋白受体的分析，常规三维定量构效分析和引入分子对接的三维定量构效分析等多个角度及不同方法提出了一个更好更加可靠的抑制剂模型。     

HLA-A*0201限制性CTL表位肽的三维定量构效关系的研究

运用比较分子力场(CoMFA)和比较分子相似性指数分析(CoMFA)方法研究了50个HLA-A^*0201限制性CTL表位九肽结构与亲和性间的关系,另外15个表位九肽作为预测集用于检验模型的预测能力.结果表明采用CoMSIA得到的构效关系模型(q^2=0.628,r^2=0.997,F=840.419)要明显优于采用CoMFA得到的构效关系模型.在CoMSIA计算中,当引入疏水场时,三维构效关系模型得到明显改善,通过该三维构效关系模型,可较精确地估算预测集中15个CTL表位肽与HLA-A^*0201间的亲和力(r^2pred=0.743).通过分析分子场等值面图在空间的分布,可以观察到表位肽分子周围的立体及疏水特征对表位肽与HLA-A^*0201间结合亲和力的影响,从而为进一步对CTL表位肽进行结构改造并基于此进行治疗性疫苗分子设计提供理论基础.     

3D-QSAR Analysis of Naphthyltriazole(Lesinurad) Analogs as Potent Inhibitors of Urate Transporter 1

To obtain useful information for identifying inhibitors of urate transporter 1(URAT1), three-dimensional quantitative structure-activity relationship(3 D-QSAR) analysis was conducted for a series of lesinurad analogs via Topomer comparative molecular field analysis(CoMFA). A 3 D-QSAR model was established using a training set of 51 compounds and externally validated with a test set of 17 compounds. The Topomer CoMFA model obtained(q^2 = 0.976, r2 = 0.990) was robust and satisfactory. Subsequently, seven compounds with significant URAT1 inhibitory activity were designed according to the contour maps produced by the Topomer CoMFA model.     

Steric and electrostatic effects in dye-cellulose interactions by the MTD and CoMFA approaches

This paper presents the application of the MTD (minimal steric difference) analysis and CoMFA (comparative molecular field analysis) to series of anthraquinone vat, mono and disazo and disperses dyes with known affinities for cellulose fiber. A comparison of the results demonstrates that these methods usually agree with the prediction of structural features favorable for dyeing. A series of n = 49 anthraquinone vat dyes was studied by MTD with r2 between 0.903 and 0.941 and r2CV values in the range of 0.827-0.878. For CoMFA, r2 = 0.992, r2CV = 0.841 were obtained; the CoMFA field is in rather good agreement with vertex attributions, by MTD for attractive and repulsive vertices. Anionic disazo dyes were studied by the CoMFA method (n = 21, r2 = 0.999, r2CV = 0.703). Monoazo dyes (several series) were studied by CoMFA and MTD. The effect of lipophilicity on dye fiber affinity was, also, studied for these dyes. Disperse dye adsorption was analyzed by MTD and CoMFA (n = 27, r2 = 0.925, r2CV = 0.776). Conclusions refer to the effect of structural features of dye molecules upon adsorption on cellulose fibers.     

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.     

3D-QSAR Analysis of a Series of Dihydroquinolizinone Derivatives as a Hepatitis B Virus Expression Inhibitor

In this study, we explored a three-dimensional quantitative structure-activity relationship(3D-QSAR) model of 63 HBV viral gene expression inhibitors containing dihydroquinolizinones. Two high predictive QSAR models have been built, including comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA). The internal validation parameter(CoMFA, q~2 = 0.701, r~2 = 0.999; CoMSIA, q~2 = 0.721, r~2 = 0.998) and external validation parameter(CoMFA, r~2_(pred = 0.999); CoMSIA, r~2_(pred = 0.999)) indicated that the models have good predictive abilities and significant statistical reliability. We designed several molecules with potentially higher predicted activity on the basis of the result of the models. This work might provide useful information to design novel HBV viral gene expression inhibitors.     

Steric and electrostatic effects in dye-cellulose interactions by the MTD and CoMFA approaches

This paper presents the application of the MTD (minimal steric difference) analysis and CoMFA (comparative molecular field analysis) to series of anthraquinone vat, mono and disazo and disperses dyes with known affinities for cellulose fiber. A comparison of the results demonstrates that these methods usually agree with the prediction of structural features favorable for dyeing. A series of n =49 anthraquinone vat dyes was studied by MTD with r 2 between 0.903 and 0.941 and r CV 2 values in the range of 0.827-0.878. For CoMFA, r 2 =0.992, r CV 2 =0.841 were obtained; the CoMFA field is in rather good agreement with vertex attributions, by MTD for attractive and repulsive vertices. Anionic disazo dyes were studied by the CoMFA method ( n =21, r 2 =0.999, r CV 2 =0.703). Monoazo dyes (several series) were studied by CoMFA and MTD. The effect of lipophilicity on dye fiber affinity was, also, studied for these dyes. Disperse dye adsorption was analyzed by MTD and CoMFA ( n =27, r 2 =0.925, r CV 2 =0.776). Conclusions refer to the effect of structural features of dye molecules upon adsorption on cellulose fibers.     

3D-QSAR and Surflex Docking Studies of a Series of Alkaline Phosphatase Inhibitors

Alkaline phosphatases(APs) include the placental AP(PLAP), germ cell AP(GCAP), intestinal AP(IAP) and tissue nonspecific AP(TNAP). Over expression of TNAP in smooth muscle cells of kidney and vessels provokes the progress of such serious diseases as end-stage renal disease, idiopathic infantile arterial calcification, ankylosis, osteoarthritis and diabetes. In order to design and optimize the potent TNAP inhibitors, comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA) were used to analyze 3D structure-activity relationships(3D-QSAR) of TNAP inhibitors. The 3D-QSAR model(CoMFA with q~2 = 0.521, r~2 = 0.930; CoMSIA with q~2 = 0.529, r~2 = 0.933) had a good predictability. Surflex-dock was used to reveal the binding mode between the inhibitors and TNAP protein. CoMFA, CoMSIA and docking results provide guidance for the discovery of TNAP inhibitors. Finally, eight new compounds as potential TNAP inhibitors were designed.     

酪氨酸蛋白磷酸酯酶1B抑制剂的分子对接和三维定量构效关系研究

用一种柔性分子对接方法(FlexX)将12个2-草酰胺苯甲酸类抑制剂和酪氨酸蛋白磷酸酯酶(PTP1B)活性口袋进行分子对接,对接程序预测的抑制剂和酶之间的相互作用能与抑制活性之间有很好的相关性(非线性相关系数R²达0.859),这说明对接结果可以比较准确地预测抑制剂和PTP1B之间的结合模式.然后,将33个同类抑制剂的骨架叠合在分子对接预测的结合构象上,用比较分子力场分析方法(CoMFA)对其进行三维定量活性构效关系研究,得到的CoMFA模型具有很好的统计相关性(交互验证回归系数q²为0.650),并可以准确地预测测试集6个化合物的活性(平均标准偏差为0.177).同时,由CoMFA模型得出的抑制剂改造信息与用FlexX预测的结合模式是一致的,进一步证明我们预测的结合模式是正确的.为研究这类抑制剂和PTP1B的结合模式及对抑制剂进行结构改造提供了信息.     

Docking and 3D-QSAR Analysis on a Series of Pyridone-based EZH2 Inhibitors

Enhancer of Zeste homolog 2(EZH2) is closely correlated with malignant tumor and regarded as a promising target to treat B-cell lymphoma. In our research, the molecular docking and three-dimensional quantitative structure-activity relationships(3D-QSAR) studies were performed on a series of pyridone-based EZH2 compounds. Molecular docking allowed us to study the critical interactions at the binding site of EZH2 protein with inhibitors and identify the practical conformations of ligands in binding pocket. Moreover, the docking-based alignment was applied to derive the reliable 3D-QSAR models. Comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA) provided available ability of visualization. All the derived 3D-QSAR models were considered to be statistically significant with respect to the internal and external validation parameters. For the CoMFA model, q~2 = 0.649, r~2 = 0.961 and r~2 pred = 0.877. For the CoMSIA model, q~2 = 0.733, r~2 = 0.980 and r~2 pred = 0.848. With the above arguments, we extracted the correlation between the biological activity and structure. Based on the binding interaction and 3D contour maps, several new potential inhibitors with higher biological activity predicted were designed, which still awaited experimental validation. These theoretical conclusions could be helpful for further research and exploring potential EZH2 inhibitors.     

Receptor-based modeling and 3D-QSAR for a quantitative production of the butyrylcholinesterase inhibitors based on genetic algorithm

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for a series of structurally related steroidal alkaloids as butyrylcholinesterase (BuChE) inhibitors. Docking studies were employed to position the inhibitors into the BuChE active site to determine the most probable binding mode. The strategy was to explore multiple inhibitor conformations in producing a more reliable 3D-QSAR model. These multiple conformations were derived using the FlexS program. The conformation selection step for CoMFA was done by genetic algorithm. The genetic algorithm based CoMFA approach was found to be the best. Both CoMFA and CoMSIA yielded significant cross-validated q(2) values of 0.701 and 0.627 and the r(2) values of 0.979 and 0.982, respectively. These statistically significant models were validated by a test set of five compounds. Comparison of CoMFA and CoMSIA contour maps helped to identify structural requirements for the inhibitors and serves as a basis for the design of the next generation of the inhibitor analogues. The results demonstrate that the combination of ligand-based and receptor-based modeling with use of a genetic algorithm is a powerful approach to build 3D-QSAR models. These data can be used for the lead optimization process with respect to inhibition enhancement which is important for the drug discovery and development for Alzheimer's disease.