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.     

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.     

新型三唑类抗真菌化合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), 系统研究了40个新型三唑类化合物抗真菌活性的三维定量构效关系. 在CoMFA研究中, 研究了两种药效构象对模型的影响, 并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、静电场、疏水场和氢键受体场的组合得到最佳模型. 所建立CoMFA和CoMSIA模型的交叉相关系数q²值分别为0.718和0.655, 并都具有较强的预测能力. CoMFA和CoMSIA模型的三维等值线图直观地解释了化合物的构效关系, 阐明了化合物结构中苯环上各位置取代基对抗真菌活性的影响, 为进一步结构优化提供了重要依据.     

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.     

取代喹啉类化合物抗菌活性的定量构效关系及分子设计

采用密度泛函理论(DFT)和逐步回归分析法对15种新合成的取代喹啉类化合物进行了定量构效关系(QSAR)研究. 在B3LYP/6-31G(d,p)水平上计算了取代喹啉的量子化学参数, 通过逐步多元回归分析筛选出影响抗菌活性的主要因素, 建立了定量构效关系方程, 并用留一法交叉分析了模型的稳定性及预测能力. 结果表明, C5的亲核电子密度fNC5及C9-N1的键级BC9-N1是影响喹啉类化合物抗金黄色葡萄球菌活性的主要因素, 所得模型对该类化合物抗菌活性有较好的预测效果. 同时基于QSAR研究结果设计了4个活性较高的新喹啉衍生物.     

4,5,6-三取代嘧啶苯磺酰脲类化合物的生物活性、分子对接与3D-QSAR关系研究

用柔性分子对接方法(FlexX)将15个4,5,6-三取代嘧啶苯磺酰脲化合物以及3个不含5-位取代嘧啶苯磺酰脲化合物(分别为4,6-双取代嘧啶和4-取代嘧啶)和乙酰羟酸合成酶(AHAS)活性口袋进行了对接, 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一定的相关性, 相关系数为0.660. 然后采用比较分子相似性指数分析(CoMSIA)对27个新型4,5,6-三取代嘧啶苯磺酰脲类化合物的除草活性进行三维定量构效关系(3D-QSAR)研究. 建立了三维定量构效关系CoMSIA模型, 立体场、静电场和氢键的贡献分别为47.3%, 32.8%, 19.9%. 交叉验证系数q2值为0.520. 根据CoMSIA模型的立体场、静电场、氢键给体场三维等值线图不仅直观地解释了结构与活性的关系, 并且与用FlexX预测的结合模式相一致, 证明了我们预测的结合模式是可靠的, 为进一步设计高活性的标题化合物提供较好的理论指导.     

具有除草活性的大环内酯类衍生物的定量构效关系

研究了一系列结构新颖的具有除草活性的大环内酯衍生物的定量构效关系(QSAR). 构建的比较分子力场分析(CoMFA)、比较分子近似指数分析(CoMSIA)和全息定量构效关系(HQSAR)分子模型的交叉验证系数r2cv均大于0.5, 非交叉验证系数r2都超过0.8, 表明获取的QSAR模型具有可信的预测能力. 对CoMFA、CoMSIA模型的三维(3D)等势图分析, 发现除了立体场和静电场外, 疏水场和氢键受体场也是影响大环内酯类化合物除草活性的重要因素. 构建的HQSAR模型的原子贡献图提示的结构改造信息与三维QSAR的结果基本一致. 利用CoMFA、CoMSIA模型提供的信息,对目前已合成的活性最高化合物B1-3进行分子结构改造, 预测结果发现部分化合物可能具有更好的除草活性.     

新型黄烷酮类衍生物的合成、杀菌活性及定量构效关系研究

设计合成了14个含不同性质取代基的黄烷酮类化合物,并采用核磁共振氢波谱、质谱和元素分析对所有化合物的结构进行了表征.在系统测定化合物对水稻稻瘟病抑制活性的IC₅₀值的基础上,采用Hansch-Fujita方法和CoMFA方法对其定量结构活性关系进行了系统研究,结果发现,化合物的疏水性质、极化效应以及最高空轨道能级对杀菌活性有重要影响,化合物的疏水参数越小,分子越容易极化,则化合物的杀菌活性越高.此外,最高空轨道能级越低,化合物越容易接受电子,其杀菌活性也相应提高.通过考察∑π、clgP以及lgK与化合物杀菌活性的相关性,发现lgK能较好地反映该类化合物的疏水效应.三维定量构效关系研究表明,B环2,3,4位上含有较大体积的取代基,而6位含较小体积的取代基,有利于提高其杀菌活性.     

2(1H)-喹啉-2,4-二酮类化合物抗小麦锈病的3D-QSAR研究

用比较分子力场分析(CoMFA)方法和比较分子相似性指数分析(CoMSIA)方法研究了21个2(1H)-喹啉-2,4-二酮类化合物抗小麦锈病的三维定量构效关系(3D-QSAR),发现用CoMFA方法可以找到最佳的3D-QSAR模型,并通过量子化学从头计算的方法研究了不同活性化合物的前线轨道及静电势分布图的差异.所得构效关系模型为发现更高活性的化合物提供理论指导.     

二肽肽酶Ⅳ抑制剂的三维定量构效关系研究

二肽肽酶Ⅳ是一类用于治疗Ⅱ型糖尿病具有潜在价值的关键酶,很多此类酶的抑制剂用于处理此病具有相当好的有效性.一系列N-取代的甘氨酰氰基吡咯烷衍生物对于二肽肽酶具有高的活性和选择性.我们使用比较分子力场分析方法建立DPP-Ⅳ抑制剂--N-取代的甘氨酰氰基吡咯衍生物的三维定量构效关系,该模型为设计用于治疗Ⅱ型糖尿病的高效DPP-Ⅳ抑制剂提供结构信息.CoMFA模型的交叉验证相关系数q2=0.575,非交叉验证相关系数r2=0.981,绝对误差S=0.184,F9.68=388.5.使用七个预测集检验了模型的预测能力.所得的模型解释了已有的构效关系,并对同类化合物有较好的预测能力,该模型可用于指导新型的DPP-Ⅳ抑制剂的设计与优化.     

Design of 5H-pyrrolo[2,3-b]pyrazine-2-phenyl Ether Derivatives as Potential JAK3 Inhibitor Based on Surflex Docking,3D-QSAR Modeling and Reverse Docking

Tyrosine protein kinase JAK3 has a very important significance on organ transplantation and the treatment of autoimmune diseases, which has been a potential therapeutic target. In recent years, a large number of JAK3 inhibitors have been reported. However, the poor selectivity and side effects have limited their widespread use in clinical practice. In order to solve this problem, 52 potential small-molecule inhibitors were combined with JAK1, JAK2 and JAK3 respectively to obtain the optimal conformation of small molecules. On the basis of that we established 3D quantitative structure-activity relationships(3D-QSAR) model. Comparative molecular field analysis(Co MFA) and molecular similarity analysis(Co MSIA) were used to evaluate the model. We took advantage of reverse docking to explore the underlying toxicity and side effects. Combining 3D quantitative structure-activity relationships, surflex-dock and reverse docking results, ten 5 H-pyrrolo[2,3-b]pyrazine-2-phenyl ether derivatives based on the most optimal selectivity and activity compound 39 were designed. It can be seen from Co MFA and Co MSIA predicted active values of designed molecules that the selectivity of designed small molecules was improved obviously. Among them, compounds 61 and 62 could become the potential small molecule compounds.     

Nonlinear dependence in comparative molecular field analysis

Summary The applicability of the comparative molecular field analysis (CoMFA) approach to describe the nonlinear dependence of biological activity on lipophilicity in 3D quantitative structure-activity relationships (QSAR) has been demonstrated. The results indicate that the CoMFA approach is appropriate for describing nonlinear effects in 3D QSAR studies.     

Studies on a Series of Coumarin Derivatives for Anticancer Activity against Breast Carcinoma Cell Line MCF-7 and Their Molecular Design

Breast cancer is one of the most common malignant tumors in women, and is also the focus of researchers. In this article, 3D-QSAR(three-dimensional quantitative structure-activity relationship) was performed on 24 molecules which are a series of coumarin derivatives for their anticancer activity. Our team divided these compounds randomly into the training and test sets to build the CoMFA(comparative molecular field analysis) and CoMSIA(comparative molecular similarity index analysis) models. The coefficients of cross-validation Q~2 and non cross-validation R~2 for CoMFA model were 0.684 and 0.949, and 0.579 and 0.930 for the CoMSIA model, respectively. The result demonstrates that the model has strong stability and satisfactory predictability. 3D contour maps suggest that the electrostatic factor has the greatest impact on activity followed by the H-bonding acceptor and hydrophilic factors. Taking the above results into account, we designed several molecules with high anticancer activity against breast carcinoma cell line MCF-7.     

Combined 3D-QSAR modeling and molecular docking study on indolinone derivatives as inhibitors of 3-phosphoinositide-dependent protein kinase-1

3-Phosphoinositide-dependent protein kinase-1 (PDK1) is a promising target for developing novel anticancer drugs. In order to understand the structure-activity correlation of indolinone-based PDK1 inhibitors, we have carried out a combined molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling study. The study has resulted in two types of satisfactory 3D-QSAR models, including the CoMFA model (r(2)=0.907; q(2)=0.737) and CoMSIA model (r(2)=0.991; q(2)=0.824), for predicting the biological activity of new compounds. The detailed microscopic structures of PDK1 binding with inhibitors have been studied by molecular docking. We have also developed docking-based 3D-QSAR models (CoMFA with q(2)=0.729; CoMSIA with q(2)=0.79). The contour maps obtained from the 3D-QSAR models in combination with the docked binding structures help to better interpret the structure-activity relationship. All of the structural insights obtained from both the 3D-QSAR contour maps and molecular docking are consistent with the available experimental activity data. This is the first report on 3D-QSAR modeling of PDK1 inhibitors. The satisfactory results strongly suggest that the developed 3D-QSAR models and the obtained PDK1-inhibitor binding structures are reasonable for the prediction of the activity of new inhibitors and in future drug design.     

苯乙酮肟醚类化合物的合成及基于定量构效关系研究的分子设计

由于肟醚结构的特殊性质, 因而很多农药和医药化合物中包含该结构. 介绍苯乙酮肟醚类化合物的合成、瓜类白粉病抑制活性的定量构效关系(QSAR)研究以及基于QSAR研究结果的新化合物设计、合成和生测, 尝试了农药先导合理设计流程在具有瓜类白粉病抑制活性的苯乙酮肟醚类化合物设计中的应用. 初步的结果显示, 介绍的工作模式既充分利用了原来的工作基础, 又降低了失败的风险、污染和成本.     

哥纳三醇类似物抗A2780肿瘤细胞活性及三维定量构效关系研究

以合成的54个哥纳三醇类似物为研究对象, 测试了其体外抑制肿瘤A2780细胞株的活性, 对其中的37个化合物用比较分子力场(CoMFA)研究了哥纳三醇类似物结构与抑制A2780肿瘤细胞活性的三维定量构效关系, 提出了对哥纳香醇甲结构改造的方法, 对寻找更好活性的化合物有重要的指导意义     

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.     

QM/MM based 3D QSAR models for potent B-Raf inhibitors

Three dimensional (3D) quantitative structure-activity relationship studies of 37 B-Raf inhibitors, pyrazole-based derivatives, were performed. Based on the co-crystallized compound (PDB ID: 3D4Q), several alignment methods were utilized to derive reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. Receptor-guided alignment with quantum mechanics/molecular mechanics (QM/MM) minimization led to the best CoMFA model (q ² = 0.624, r ² = 0.959). With the same alignment, a statistically reliable CoMSIA model with steric, H-bond acceptor, and hydrophobic fields was also derived (q ² = 0.590, r ² = 0.922). Both models were validated with an external test set, which gave satisfactory predictive r ² values of 0.926 and 0.878, respectively. Contour maps from CoMFA and CoMSIA models revealed important structural features responsible for increasing biological activity within the active site and explained the correlation between biological activity and receptor-ligand interactions. New fragments were identified as building blocks which can replace R1-3 groups through combinatorial screening methods. By combining these fragments a compound with a high bioactivity level prediction was found. These results can offer useful information for the design of new B-Raf inhibitors.     

Different binding modes of structurally diverse ligands for human D3DAR

Five different dopamine D3 receptors (D3DARs) models were created considering some suggested binding modes for D3DAR antagonists reported in earlier computational studies. Different hypotheses are justified because of the lack of experimental information about the putative site of interaction and are also due to the variability in scaffolds and size of D3DAR ligands. In this study 114 potent and selective D3DAR antagonists or partial agonists are used as key experimental information to discriminate the most reliable receptor model and to build a docking based 3D quantitative structure-activity relationship model able to indicate the ligand properties and the residues important for activity. The ability of this D3DAR model to discriminate the binding mode of different classes of ligands, showing a good quantitative correlation with their activity, encourages us to use it for screening novel lead compounds.