HIV-1逆转录酶抑制剂的3D-QSAR研究及分子设计

采用Topomer Co MFA方法对24个二芳基苯胺衍生物进行三维定量构效关系研究,建立了3DQSAR模型,所得优化模型的非交叉相关系数、交互验证系数以及外部验证的复相关系数分别为0.928,0.654和0.940,结果表明该模型具有良好的稳定性和预测能力。采用分子对接技术对药物与受体的作用机制进行了研究,结果显示,药物与HIV-1逆转录酶的LYS172,GLU138,LYS101等位点作用明显。运用这些信息进行分子设计,在理论上获得了一些具有较高活性的新的二芳基苯胺类抗艾滋病药物,该QSAR的研究结果可为新药合成提供理论参考。     

R基团搜索技术用于硼酸三肽蛋白酶体抑制剂的分子设计

本文采用Topomer CoMFA对44个Tyropeptin硼酸三肽类蛋白酶体抑制剂进行三维定量构效关系(3D-QSAR)分析。所得最优模型的拟合、交互验证、及外部验证的复相关系数分别为0.983、0.651、0.963。采用Topomer search对ZINC数据库进行R基团的虚拟筛选,得到具有特定活性贡献的R基团,以活性最高的分子为模板过滤,得到7个R1和5个R2基团。并以此设计得到活性优于模板分子的20个新化合物。结果表明,所建立的Topomer CoMFA模型具有良好的稳定性和预测能力,基于R集团的Topomer search技术可以有效筛选,并为设计出新的蛋白酶体抑制剂提供理论依据。     

R基团搜索技术用于PTH类Tau蛋白抑制剂的分子设计

对26个PTH类Tau蛋白抑制剂进行了Topomer CoMFA研究, 建立了拟合及预测能力良好的Topomer CoMFA模型, 获得的模型拟合、 交互验证及外部预测的复相关系数分别为0.976, 0.603和0.795, 估计标准偏差和Fisher验证值F分别为0.110和115.778. 使用ZINC化合物数据集作为结构片段源, 通过三维定量构效关系(3D-QSAR)模型搜索具有特定活性贡献的R基团. 以样本中活性最高的1号分子过滤, R₁和R₂贡献值均提高了20%的片段分别有9个与2个. 以此交替取代1号样本的R₁与R₂, 得到18个新颖化合物并预测其活性, 其中的15个预测活性值优于模板分子. 研究结果表明, Topomer search可有效地用于分子设计, 所设计的分子为阿尔茨海默病(AD)药物的研发提供了新的候选物.     

芳香噻嗪类衍生物作为选择性醛糖还原酶抑制剂的分子对接和基于受体的三维定量构效关系研究

芳香噻嗪类衍生物被证明是一类选择性较好的高活性醛糖还原酶抑制剂(ARIs).本文对44个芳香噻嗪类化合物进行了分子对接(docking)和三维定量构效关系(3D-QSAR)研究,并探索了此类化合物与醛糖还原酶(ALr²)的作用机理.醛糖还原酶与醛还原酶(ALR1)活性位点的叠加结果显示, ALr²中残基Leu 300和Cys298的存在是化合物1m具有高选择性的原因.分别建立了比较分子场分析方法(CoMFA, q² = 0.649, r² =0.934; q²:交叉验证相关系数, r²:非交叉验证相关系数)和比较分子相似性指数分析方法(CoMSIA, q² = 0.746, r² = 0.971)模型,并对影响此类化合物生物活性的结构进行了鉴定.结果显示,两个模型均具有较高预测能力,并通过测试集中的7个化合物进行了验证,其中CoMFA模型和CoMSIA模型的预测相关系数(r_Pred²)分别为0.748和0.828. 3D-QSAR模型中的三维等值线图表明,在化合物1m的苄基环上C3和C4位置以及苯并噻嗪母核上C5和C7位置进行改进可能对生物活性的提高有利,此预测与我们前期报道的苯并噻嗪母核C7位改进结果一致.本文所建3D-QSAR模型能够在理性设计具有更高生物活性的新型ARIs中发挥重要作用.     

3D-QSAR and Docking Studies of Pyrido[2,3-d]pyrimidine Derivatives as Wee1 Inhibitors

In order to investigate the inhibiting mechanism and obtain some helpful information for de-signing functional inhibitors against Wee1, three-dimensional quantitative structure-activity relationship (3D-QSAR) and docking studies have been performed on 45 pyrido[2,3-d] pyrim-idine derivatives acting as Wee1 inhibitors. Two optimal 3D-QSAR models with significant statistical quality and satisfactory predictive ability were established, including the CoMFA model (q²=0.707, R²=0.964) and CoMSIA model (q²=0.645, R²=0.972). The external val-idation indicated that both CoMFA and CoMSIA models were quite robust and had high predictive power with the predictive correlation coefficient values of 0.707 and 0.794, essen-tial parameter r²_m values of 0.792 and 0.826, the leave-one-out r²_m(LOO) values of 0.781 and 0.809, r²_m(overall) values of 0.787 and 0.810, respectively. Moreover, the appropriate binding orientations and conformations of these compounds interacting with Wee1 were revealed by the docking studies. Based on the CoMFA and CoMSIA contour maps and docking analyses, several key structural requirements of these compounds responsible for inhibitory activity were identified as follows: simultaneously introducing high electropositive groups to the sub-stituents R1 and R5 may increase the activity, the substituent R2 should be smaller bulky and higher electronegative, moderate-size and strong electron-withdrawing groups for the substituent R3 is advantageous to the activity, but the substituent X should be medium-size and hydrophilic. These theoretical results help to understand the action mechanism and design novel potential Wee1 inhibitors.     

含1,6-二氢-6-哒嗪酮的1,3,4-噁二唑啉和噻唑烷-4-酮类化合物的合成

以1,6-二氢-6-哒嗪酮-3-甲酰肼(1)与芳香醛反应得到相应的1,6-二氢-6-哒嗪酮-3-羰基芳香醛腙(2a～2e). 再将2a～2e与乙酸酐作用, 合环得到一系列含有1,3,4-噁二唑啉环的衍生物3a～3e; 在DMF中用HSCH₂COOH合环, 得到含有4-噻唑烷酮的衍生物4a～4e. 所有化合物的结构均经元素分析, IR, ¹H NMR 和MS谱得以证实.     

Design of Novel IRAK4 Inhibitors Using Molecular Docking,Dynamics Simulation and 3D-QSAR Studies

Treatment of several autoimmune diseases and types of cancer has been an intense area of research over the past two decades. Many signaling pathways that regulate innate and/or adaptive immunity, as well as those that induce overexpression or mutation of protein kinases, have been targeted for drug discovery. One of the serine/threonine kinases, Interleukin-1 Receptor Associated Kinase 4 (IRAK4) regulates signaling through various Toll-like receptors (TLRs) and interleukin-1 receptor (IL1R). It controls diverse cellular processes including inflammation, apoptosis, and cellular differentiation. MyD88 gain-of-function mutations or overexpression of IRAK4 has been implicated in various types of malignancies such as Waldenström macroglobulinemia, B cell lymphoma, colorectal cancer, pancreatic ductal adenocarcinoma, breast cancer, etc. Moreover, over activation of IRAK4 is also associated with several autoimmune diseases. The significant role of IRAK4 makes it an interesting target for the discovery and development of potent small molecule inhibitors. A few potent IRAK4 inhibitors such as PF-06650833, RA9 and BAY1834845 have recently entered phase I/II clinical trial studies. Nevertheless, there is still a need of selective inhibitors for the treatment of cancer and various autoimmune diseases. A great need for the same intrigued us to perform molecular modeling studies on 4,6-diaminonicotinamide derivatives as IRAK4 inhibitors. We performed molecular docking and dynamics simulation of 50 ns for one of the most active compounds of the dataset. We also carried out MM-PBSA binding free energy calculation to identify the active site residues, interactions of which are contributing to the total binding energy. The final 50 ns conformation of the most active compound was selected to perform dataset alignment in a 3D-QSAR study. Generated RF-CoMFA (q² = 0.751, ONC = 4, r² = 0.911) model revealed reasonable statistical results. Overall results of molecular dynamics simulation, MM-PBSA binding free energy calculation and RF-CoMFA model revealed important active site residues of IRAK4 and necessary structural properties of ligand to design more potent IRAK4 inhibitors. We designed few IRAK4 inhibitors based on these results, which possessed higher activity (predicted pIC₅₀) than the most active compounds of the dataset selected for this study. Moreover, ADMET properties of these inhibitors revealed promising results and need to be validated using experimental studies.     

3-氨磺酰苯甲酸类AKR1C3抑制剂的3D-QSAR和分子对接研究

醛酮还原酶1C3(AKR1C3)作为治疗前列腺癌的新靶点已成为研究热点,3-氨磺酰苯甲酸衍生物对其具有高效的选择性和抑制活性。本文采用比较分子场分析(COMFA)和比较分子相似性指数分析(COMSIA)方法,将经分子对接后的34个优势构象组成训练集和11个优势构象组成测试集,构建三维定量构效关系(3D-QSAR)模型。COMFA模型的交叉验证系数(q2),非交叉验证系数(R2),标准偏差(SEE)和F值分别为0.761,0.973,0.122,185.963;自举法回归系数为R2bs=0.98。最佳组合COMSIA模型的q2,R2,SEE,F和R2bs分别为0.734,0.984,0.097,147.850,0.994。COMFA和COMSIA模型的系统外部测试R2pred分别为0.864和0.756,r2m分别为0.8127和0.5377。这些结果表明,所建立的QSAR模型具有较高的可靠性和较强预测能力。经三维等势图分析可知,在2、5或6位适当增加取代基体积,或在5位引入氢键受体,或在7位引入负电性取代基则能提高化合物的生物活性。该模型为进一步设计具有更优选择性和活性的化合物提供了理论依据。     

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.     

含呋喃环双酰脲类衍生物的三维定量构效关系研究

采用比较分子力场分析法(CoMFA)和比较分子相似性指数分析法(CoMSIA), 对27个新型双酰基脲类化合物的杀蚊幼虫(Aedes aegypti L.)活性进行三维定量构效关系(3D-QSAR)研究. 在CoMFA研究中, 考察了网格点步长对统计结果的影响. 在CoMSIA研究中, 系统考察了各种分子场组合、网格点步长和衰减因子对模型统计结果的影响, 发现立体场和氢键供体场的组合得到最佳模型. 所建立的CoMFA和CoMSIA模型的非交叉验证相关系数r2值分别为0.828和0.841, 并都具有较强的预测能力. CoMFA和CoMSIA模型的三维等值图不仅直观地解释了结构与活性的关系, 而且为后续优化该系列化合物提供了理论依据.     

二芳基三嗪类HIV-1逆转录酶抑制剂的分子对接及3D-QSAR研究

对40个二芳基三嗪类HIV-1逆转录酶抑制剂进行了分子对接研究,结果表明,2个疏水性芳香取代基团与结合口袋底部形成的疏水和范德华相互作用、三嗪环母核及其R4取代基与结合位点产生的氢键和静电作用以及R4取代基与袋口形成的空间位阻效应是影响该系列化合物活性的重要因素.根据对接优势构象进行分子叠合和比较分子相似性指数分析(C...     

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.     

3D-QSAR Studies on the Anti-tumor Activity of N-Aryl-salicylamide Derivatives

In the present work, comparative molecular field analysis(CoMFA) techniques were used to perform three-dimensional quantitative structure-activity relationship(3 D-QSAR) studies on the anti-tumor activity(pHi, i = 1, 2, 3, 4) of N-aryl-salicylamide derivatives against four cancer cell lines, including A549, MCF-7, SGC-7901, and Bel-7402. 12 compounds were randomly selected as the training set to establish the prediction models, which were verified by the test set of 5 compounds containing template molecule. The contributions of steric and electrostatic fields to pH1, pH2, pH_3, and pH_4were 23.8% and 76.2%, 20.1% and 79.9%, 18.7% and 81.3%, and 14.3% and 85.7%, respectively. The cross-validation(Rcv2) and non-cross-validation coefficients(R2) were 0.826 and 0.963 for pH1, 0.867 and 0.974 for pH2, 0.941 and 0.989 for pH_3, and 0.797 and 0.961 for pH_4, respectively. The CoMFA models were then used to predict the activities of the compounds, and it was found that the models had strong stability and good predictability. Based on the CoMFA contour maps, some key structural factors responsible for the anticancer activity of the series of compounds were revealed. The results provide some useful theoretical references for understanding the mechanism of action, designing new N-aryl-salicylamide derivatives with high anti-tumor activity, and predicting their activities.     

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.     

3D-QSAR Studies on a Series of Indoleamide Derivatives as Antiplasmodial Drugs

In this study, three-dimensional quantitative structure-activity relationship(3D-QSAR) was studied for the antiplasmodial activity of a series of novel indoleamide derivatives by comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(Co MSIA). 3D-QSAR model was established by a training set of 20 compounds and was externally validated by a test set of 4 compounds. The best prediction(Q~2 = 0.593 and 0.527, R~2 = 0.990 and 0.953, r_(pred)~2 = 0.967 and 0.962 for CoMFA and CoMSIA) was obtained according to CoMFA and CoMSIA. Those parameters indicated the model was reliable and predictable. We designed several molecules with high activities according to the contour maps produced by the CoMFA and CoMSIA models.     

苯并噁嗪酮衍生物的3D-QSAR分析

苯并噁嗪酮衍生物是近年来发现的一类抗血小板聚集化合物,在前人研究的基础上利用比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)对23个苯并噁嗪酮衍生物进行了三维定量构效关系(3D-QSAR)研究.其中CoMFA模型交叉验证系数Q2=0.703,回归系数R2=0.994,计算值与实验值的平均方差SEE=0.053,统计方差比F=184.773;CoMSIA模型Q2=0.847,R2=0.992,SEE=0.058,F=171.670.两种方法得到的模型都具有较好的预测能力.结果表明,标题化合物中8-位取代基R1静电效应起主要作用;2-位取代基R2立体效应占主导作用,但官能团大小要适中.根据研究结果设计了六种活性较高的化合物.     

苯并嗪酮衍生物的3D-QSAR分析

苯并嗪酮衍生物是近年来发现的一类抗血小板聚集化合物, 在前人研究的基础上利用比较分子场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)对23个苯并嗪酮衍生物进行了三维定量构效关系(3D-QSAR)研究. 其中CoMFA模型交叉验证系数Q2=0.703, 回归系数R2=0.994, 计算值与实验值的平均方差SEE=0.053, 统计方差比F=184.773; CoMSIA模型Q2=0.847, R2=0.992, SEE=0.058, F=171.670. 两种方法得到的模型都具有较好的预测能力. 结果表明, 标题化合物中8-位取代基R1静电效应起主要作用; 2-位取代基R2立体效应占主导作用, 但官能团大小要适中. 根据研究结果设计了六种活性较高的化合物.     

含呋喃环双酰肼类衍生物的合成、杀虫活性及3D-QSAR研究

为发现新的昆虫生长调节剂,经单取代苯基呋喃甲酰氯与取代苯甲酰肼反应得到22个未见文献报道的含呋喃环双酰肼类化合物,其结构均通过了IR,1HNMR和元素分析确认.初步生测结果表明,所有目标化合物对豆蚜(Aphisfabae)均有活性,部分目标化合物表现出较好或中等的杀幼虫活性.化合物Ia,Ib和Ic在药剂浓度为0.05%时,对豆蚜的死亡抑制率分别为81.8%,58.4%和52.2%,其中化合物Ia对若蚜的蜕皮和成蚜产雌能力具有一定的抑制作用.而大部分目标化合物在药剂浓度为0.1%,0.05%和0.001%时,对3龄粘虫(Mythimna separate)、棉红蜘蛛(Tetranchus urticae)和尖音淡色库蚊(Culex pipiens pallens)幼虫杀虫活性不明显.采用比较分子力场分析(CoMFA)方法,对22个化合物的杀蚜虫活性进行三维定量构效关系(3D-QSAR)研究.在CoMFA研究中,考察了不同力场和电荷下网格点步长对统计结果的影响.建立了三维定量构效关系CoMFA模型(q2=0.518,r2=0.936).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,而且为后续优化该系列化合物提供了理论依据.