楝酰胺(Rocaglamide)类化合物的三维定量构效关系

采用比较分子力场分析(CoMFA)和比较分子相似因子分析(CoMSIA)方法,对训练集中的26个楝酰胺(Rocaglamide)类化合物进行了三维定量构效关系(3D-QSAR)研究,最终建立的CoMFA模型和CoMSlA模型的q<'2>分别为0.593和0.656.并对测试集中的5个化合物的生物活性进行了预测,结果表明...     

拓扑异构酶Ⅰ抑制剂高喜树碱类化合物的三维定量构效关系研究

针对拓扑异构酶Ⅰ抑制剂高喜树碱类化合物,采用比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)的方法对其进行三维定量构效关系的研究.构建CoMFA模型其q2=0.706,最佳主成分数n=6,非交叉验证系数r2=0.966,标准差S=0.277,F=117.613,立体场和静电场的贡献值分别为0.62和0.38.构建CoMSIA模型其q2=0.696,最佳主成分数n=7,非交叉验证系数r2=0.956,标准差S=0.320,F=74.374,其疏水场和立体场的贡献分别为0.75和0.25.结果显示疏水场和立体场对化合物的活性有较大影响.     

Computer-aided molecular design of (E)-N-Aryl-2-ethene-sulfonamide analogues as microtubule targeted agents in prostate cancer

Microtubules are tube-shaped, filamentous and cytoskeletal proteins that are essential in all eukaryotic cells. Microtubule is an attractive and promising target for anticancer agents. In this study, three-dimensional quantitative structure activity relationships (3D-QSAR) including comparative molecular field analysis, CoMFA, and comparative molecular similarity indices analysis, CoMSIA, were performed on a set of 45 (E)-N-Aryl-2-ethene-sulfonamide analogues as microtubule-targeted anti-prostate cancer agents. Automated grid potential analysis, AutoGPA module in Molecular Operating Environment 2009.10 (MOE) as a new 3D-QSAR approach with the pharmacophore-based alignment was carried out on the same dataset. AutoGPA-based 3D-QSAR model yielded better prediction parameters than CoMFA and CoMSIA. Based on the contour maps generated from the models, some key features were identified in (E)-N-Aryl-2-arylethene-sulfonamide analogues that were responsible for the anti-cancer activity. Virtual screening was performed based on pharmacophore modeling and molecular docking to identify the new inhibitors from ZINC database. Seven top ranked compounds were found based on Gold score fitness function. In silico ADMET studies were performed on compounds retrieved from virtual screening in compliance with the standard ranges.     

两类促肾上腺皮质释放激素(CRH)拮抗剂的分子场分析

采用比较分子相似性指数分析方法(CoMSIA)及比较分子场分析方法(CoMSIA)研究了两组CRH拮抗剂结构与活性的关系。在两种方法中,都考虑了静电场、立体场以及氢键场对构效关系的影响,结果表明采用CoMSIA得到构效关系模型要明显优于采用CoMFA得到的构效关系模型,在CoMSIA计算中,当引入疏水场时,三维构效关系模型能得到明显的改善,通过这个三维构效关系模型,可以较为精确地预测化合物的活性。通过分析分子场等值面图在空间的分布,可以观察到叠合分子周围的立体、静电以及疏水特征对化合物活性的影响。     

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.     

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.     

STAT3环烷烃并噻吩类衍生物抑制剂的三维定量构效关系研究

本文对STAT3抑制剂的化学结构与生物活性之间的关系进行研究。采用三维定量构效关系(3D-QSAR)中的比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)方法针对52个STAT3抑制剂建立3D-QSAR模型,阐明了抑制剂化学结构与其生物活性之间的关系。所构建的CoMFA模型交叉验证系数为0.548,非交叉验证系数为0.754,标准偏差为0.278,显著系数为58.297;所构建的CoMSIA模型交叉验证系数为0.892,非交叉验证系数为0.597,标准偏差为0.192,显著系数为57.794。结果显示CoMFA和CoMSIA模型具有良好的稳定性和预测能力。3D-QSAR模型等势图提供的相关场信息对新型STAT3抑制剂的设计具有指导意义。     

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.     

3D-QSAR Analysis of a Series of 1,2,3-Triazole-chromenone Derivatives as an Acetylcholinesterase Inhibitor against Alzheimer's Disease

Chromenones have attracted much attention since they are excellent acetylcholinesterase inhibitor(AChEi). The 1,2,3-triazoles are multifunctional anti-acetylcholinesterase(AChE) agents. In this paper, we report the three-dimensional quantitative structure-activity relationship(3D-QSAR) study of 25 1,2,3-triazolechromenone derivatives based comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA). To construct CoMFA and CoMSIA models, the 25 active molecules were randomly divided into the training and test sets. The obtained cross-validation Q~2 of the CoMFA model, the coefficient of non-cross-validation R~2, and the test value F are 0.597, 0.994, and 396.726, respectively. The cross-validation Q~2 of the CoMSIA model, the coefficient of the non-cross-validation R~2, and the test value F are 0.721, 0.979, and 131.107, respectively. The predictive correlation coefficient(r_(pred)~2) is 0.728 for CoMFA and 0.805 for CoMSIA, which verifies that the model is predictable. Based on the potential maps of CoMFA and CoMSIA, a library containing a set of potent AChEi was designed. The inhibitory potential of the compounds in this library was found to be greater than the inhibitory potential of the most active compounds in the data set. The results obtained from this study laid the foundation for the development of effective drugs for AChEi.     

Molecular Modeling Studies of Vascular Endothelial Growth Factor Receptor Tyrosine Kinase Inhibitors Combining Molecular Docking and 3D-QSAR Methods

The vascular endothelial growth factor (VEGF) and its receptor tyrosine kinases VEGFR-2 or kinase insertdomain receptor (KDR) have emerged as attractive targets for the design of novel anticancer agents. In the present work, molecular docking method combined with three dimensional quantitative structure-activity relationships (comparative molecular field analysis (CoMFA) and comparative molecular similarity indice analysis (CoMSIA)) to analyze the possible interactions between KDR and those derivatives which acted as selective inhibitors. The CoMFA and CoMSIA models gave a cross-validated coefficient Q2 of 0.713 and 0.549, non-cross-validated R2 values of 0.974 and 0.878, and predicted R2 values of 0.966 and 0.823, respectively. The 3D contour maps generated by the CoMFA and CoMSIA models were used to identify the key structural requirements responsible for the biological activity. The information obtained from 3D-QSAR and docking studies were very helpful to design novel selective inhibitors of KDR with desired activity and good chemical property.     

3 D-QSAR Analysis of Agonists of nAChRs： Epibatidine Analogues

A 3 D-QSAR about nAChRs agonists epibatidine analogues was performed using theCoMFA and CoMSIA. The correlation coefficients were R2cv = 0.546, R2cv = 0.907 in CoMFA andR2cv = 0.655, R2,~ = 0.962 in CoMSIA of the final model. The prediction using the final models tothe test set was r2 = 0.675 in CoMFA and r2 = 0.462 in CoMSIA. This model will be useful in thedesign of novel compounds with high affinity.     

Discodermolide及其衍生物三维定量构效关系的研究

Discodermolide是一种新颖的作用于微管蛋白的抗肿瘤化合物, 具有良好的药用前景. 为了设计出药效更好的类似物, 我们用比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)对discodermolide及其衍生物进行了三维定量构效关系(3D-QSAR)的研究, 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(q2)为0.592, 非交叉验证相关系数(r2)为0.982, 标准偏差(SEE)为0.094, F值为119.761; CoMSIA模型的q2为0.544, r2为0.980, SEE为0.098, F值为108.715. 计算结果表明, 获得的CoMFA和CoMSIA模型具有良好的预测能力, 可以应用于指导该类化合物的设计.     

一类吡唑衍生物的3D-QSAR研究

通过比较分子力场分析方法（CoMFA）和比较分子相似性指数分析方法 (CoMSIA),系统研究了30个2-烷基（烷硫基）-5-吡唑基-1,3,4-噁二唑（噻二? 颉⑷颍├嗷衔镆种扑疚瓶莶【锘钚缘娜酆隙抗剐Ч叵怠６杂? CoMFA,研究了不同移动步长对考虑静电场和立体场作用时构效关系的影响;对于 CoMSIA,研究了移动步长、场的组合、衰减因子α等参数变化对构效关系的影响, 发现当考虑立体场、疏水场、氢键受体场的贡献时能得到较好的结果。分别得到了 两种方法最为理想的3D-QSAR模型,所得三维等值线图为发现更高活性化合物提供 了有力的指导作用。     

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.     

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 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.     

五味子素类抑制HIV活性的三维定量构效关系研究

建立了五味子活性成分木脂素类和联苯类化合物抑制HIV活性的三维定量构效 方程。采用联苯环原子和苯环质心两种叠合方式，并区分联苯化合物的不同构型， 共建立了四类CoMSIA模型，其中训练集中联苯类为S构型并叠合联苯环原子建立的 CoMSIA模型相关性最好，交叉验证相关系数q~2为0.71，非交叉验证相关系数r~2 = 0.99，标准偏差SE = 0.051, F = 1000.6。CoMSIA方法采用Gaussian函数计算 场能，并在CoMFA方法的立体和静电场基础上加入疏水场，PLS分析结果更为准确。 该模型三维等势图证实了某些结构和活性规律，如联苯基共面性越好，活性越高， 同时给出了苯环上取代基的体积、电性和疏水性要求，为该类化合物的结构改造提 供了依据。     

3D-QSAR Study of Melittin and Amoebapore Analogues by CoMFA and CoMSIA Methods

Peptides are one of the indispensable substances in life. The use of computer aided drug design(CADD) methods to design peptides and peptiodmimetics can short the design cycle, save research funding, improve the level of whole research to a large extent and guide the discovery of new drugs. In this paper, Melittin and amoebapore three-dimensional quantitative structureactivity relationship(3D-QSAR) models were established by using comparative molecular field analysis(CoMFA) and comparative molecular similarity indices analysis(CoMSIA) method. The result shows that, the correlation coefficient(q~2) was 0.583 and non-cross-validation correlation coefficient(r~2) was 0.972 for the melittin CoMFA model. The q~2 and r~2 were 0.630 and 0.995 for the best CoMSIA model, 0.645 and 0.993 for the amoebapore CoMFA model, and 0.738 and 0.996 for the best CoMSIA model. The statistical parameters demonstrated that the CoMFA and CoMSIA models had both good predictive ability and high statistical stability, and can provide theoretical basis for designing new high activity polypeptide drugs.