N,N-二甲基-2-溴苯乙胺类衍生物活性与CoMFA、CoMSIA的研究

用比较分子力场分析(CoMFA)法和比较分子相似性指数分析(CoMSIA)法,建立N,N-二甲基-2-溴苯乙胺类化合物的3D-QSAR模型。CoMFA模型中,其交叉验证系数q2=0．792,传统的相关系数R2=0．955(R=0．978),相应立体场贡献为77．4％、静电场贡献为22．6％,优于文献的报导。CoMSIA研究中,其交叉验证系数q2=0．757,传统的相关系数R2=0．917 (R=0．958),其疏水场、立体场、静电场贡献依次为:42．9％、39．5％、17．6％。用两种模型分别预测检测集分子的活性,结果与实验值较吻合。说明所建的模型具有较好的预测能力。通过分析CoMFA分子场等值线图,可为优化N,N-二甲基-2-溴苯乙胺类衍生物的结构提供理论指导。     

噻唑类Fascin蛋白抑制剂的3D-QSAR研究

本文针对43个噻唑衍生物Fascin蛋白抑制剂,运用CoMFA(比较分子力场分析)以及CoMSIA(比较分子相似性指数分析)这两种经典的3D-QSAR方法,建立了CoMFA模型和CoMSIA模型,分别对其进行三维定量构效关系研究。CoMFA模型和CoMSIA模型的交叉验证系数q~2分别为0.731和0.846,相关系数r~2分别为0.969和0.926。这两种模型都显示出了比较好的预测性和稳定性。它们的三维等势图以及对接结果也证实了抑制剂活性和结构特征之间的关系,可以为今后设计研究新型Fascin抑制剂而提供了理论基础。     

N，N-二甲基二2-溴苯乙胺类衍生物活性与CoMFA、CoMSIA的研究

用比较分子力场分析（CoMFA）法和比较分子相似性指数分析（CoMSIA）法，建立N，N-二甲基-2-溴苯乙胺类化合物的3D—QSAR模型。CoMFA模型中，其交叉验证系数q^2=0．792，传统的相关系数R^2=0．955（R=0．978），相应立体场贡献为77．4％、静电场贡献为22．6％，优于文献的报导。CoMSIA研究中，其交叉验证系数q^2=0．757，传统的相关系数R^2=0．917（R=0．958），其疏水场、立体场、静电场贡献依次为：42．9％、39．5％、17．6％。用两种模型分别预测检测集分子的活性，结果与实验值较吻合。说明所建的模型具有较好的预测能力。通过分析CoMFA分子场等值线图，可为优化N，N-二甲基-2-溴苯乙胺类衍生物的结构提供理论指导。     

苯酰胺类组蛋白去乙酰化酶抑制剂的分子对接及三维定量构效关系研究

利用surflex-dock模块对59个苯酰胺类抑制剂和组蛋白去乙酰化酶进行了对接研究,分析了配体和受体的相互作用模式,所得结论与文献报道的实验结果符合。并利用比较分子力场分析方法(comparative molecular field anal-ysis,CoMFA)对此类抑制剂分子进行了三维定量构效关系研究,所建模型交叉验证相关系数q~2=0.640,非交叉验证相关系数r~2=0.932,有较好的预测能力。CoMFA得到的立体场和静电场的等值线图可用于指导新型药物的设计与合成。     

苯甲酰氨类HDAC2抑制剂的3D-QSAR及虚拟筛选研究

本文应用传统比较分子力场分析法CoMFA,比较分子相似性指数法CoMSIA和Topomer CoMFA方法,对组蛋白去乙酰化酶2(HDAC2)的苯甲酰胺类抑制剂进行了构效关系和基于药效团的筛选研究。基于分子片段建模的Topomer CoMFA的交叉验证系数q~2为0.594,预测相关系数r~2_(pred)为0.973。基于对接活性构象叠合得到的CoMFA,CoMSIA的交叉验证相关系数q~2分别为0.634,0.561,预测相关系数r~2_(pred)分别为0.905,0.68。基于药效团模型011叠合的CoMFA,CoMSIA交叉验证相关系数q~2分别为0.588,0.592,预测相关系数r~2_(pred)分别为0.68,0.859。结果表明这5个3D-QSAR模型均具有良好的稳定性和预测能力。另外,由18个活性较高结构多样的分子建立了可靠的药效团模型。运用药效团模型011和016对NCI数据库进行筛选,将筛选得到的分子与HDAC2蛋白酶进行分子对接,并由PASS进行活性验证,最终得到了18个分子,且对接打分值都大于6,可作为新的HDAC2抑制剂。     

HMG-CoA还原酶抑制剂的3D-QSAR研究:基于分子对接和FlexS的叠合

HMG-CoA还原酶是降血脂药物设计的重要靶标,抑制该酶的活性可以有效地降低血浆总胆固醇水平,从而降低心脑血管疾病的发病几率。拜斯亭事件以后,他汀类药物的安全性特别是长期服用的安全性一直备受关注,所以,设计新型安全的HMGR抑制剂仍然十分迫切。本文利用已经建立的分子对接模型对接文献中已经报道的几组HMGR抑制剂分子,确定这些分子可能的结合构象。然后,利用比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)研究其三维定量构效关系,所建CoMFA、CoMSIA模型的交叉验证相关系数q~2分别为0.625和0.683(10组CV),对测试集化合物的活性预测结果与实验数据相关性很好,表明模型预测能力较强。分析出三维空间中各种分子场(立体、静电、疏水、氢键)的有利位置。同时,论文还采用FlexS的叠合方式构建CoMSIA模型,比较3D-QSAR研究中分子对接和分子场的叠合。     

DNA依赖蛋白激酶抑制剂的CoMFA研究

运用比较分子力场分析方法(CoMFA),以DNA依赖蛋白激酶(DNA-PK)抑制剂分子为研究对象,建立1组对DNA依赖蛋白激酶有抑制活性化合物的三维定量构效关系(3D-QSAR)模型,探索其活性数据和三维结构参数的关系,所建最佳模型交叉验证相关系数q2=0.670,非交叉验证相关系数R2=0.993,标准偏差SD=0.053,说明该模型预测能力较好.根据CoMFA模型的三维等势图可知,小体积、电负性大的取代基团,能提高该类化合物的活性,为新型DNA-PK抑制剂分子的设计提供了理论依据.     

类青蒿素抗疟活性3D-QSAR研究

采用比较分子力场分析(CoMFA)法,将40个类青蒿素的抗疟活性作了定量构效关系的研究。所得模型交叉验证系数q2为0．601,非交叉验证系数r2为0．982,标准偏差SE=0．140,F=302．246,其中立体场与静电场的贡献分别为33．0％和67．0％。CoMFA离散图表明,增大C13上取代基的体积以及增强O15上取代基的电负性等,都有利于提高化合物的抗疟活性。该模型的离散图为改造此类化合物的结构提供理论依据和研究方向。     

2,5-二酮哌嗪类微管蛋白抑制剂的3D-QSAR研究

微管蛋白对细胞增殖极为重要,现已成为抗癌药物研发的重要靶标之一。针对53个以2,5-二酮哌嗪为基本骨架的微管蛋白抑制剂,分别运用比较分子力场分析(CoMFA)以及比较分子相似性指数分析(CoMSIA)2种经典方法进行了三维定量构效关系(3D-QSAR)研究,并依次建立了相关的模型。CoMFA模型的交叉验证系数q~2为0.642,相关系数r~2为0.996:CoMSIA模型的q~2和r~2,分别为0.725,0.908。模型具有较好的预测能力和较强的稳定性。3D-QSAR模型三维等势图揭示了一些结构特征与抑制活性的关系。我们希望这些研究为该类药物今后的设计和筛选提供可靠的理论依据。     

Chk2抑制剂2-芳香基苯并咪唑类化合物活性的CoMFA研究

利用比较分子力场分析(CoMFA)法,研究训练集中的37个Chk2抑制剂2-芳香基苯并咪唑类化合物的生物活性,考察了互变异构对抑制剂活性的影响,建立了主成分为4的三维定量结构-活性关系模型.模型的交叉和非交叉验证回归系数(q2、r2)分别为0.660和0.908,是稳定性较高和预测能力较好的模型,立体场对活性的影响比静电场大.模型可用于指导设计新的Chk2抑制剂.     

蛙皮降压肽类似物构效关系的研究

蛙皮降压肽(Sauvagine,Svg)是一种良好的促肾上腺皮质激素,释放因子2受体(CRF2R)激动剂,但它是一种非选择性激动剂,作用于CRF1R而产生副作用。为了探讨蛙皮降压肽类似物对CRF2R与CRF1R选择性的影响,本文使用正交因子(Orthogonal Factors,Orth-Factors)描述子,对45个蛙皮降压肽类似物与CRF2F、CRF1R的亲合力进行了QSAR研究,所建立的QSAR模型预测能力良好。通过QSAR模型信息,设计了17个蛙皮降压肽类似物,其中3个对CRF2R与CRF1R受体选择性良好(SQ:110,TQ:105,VQ:110),有进一步研究的价值。     

CoMFA analysis on a set of new noncompetitive GABAA receptor antagonists： fipronil and related analogs

Fipronil and related analogs, a set of new noncompetitive GABAA receptor antagonists, were investigated using comparative molecular field analysis （CoMFA） to explore their three-dimensional quantitative structure-activity relationships （3D-QSAR）. Considering the structural complexity of molecules of fipronil and related analogs, three different alignments were performed in this paper. CoMFA model for housefly receptor yield the leave-one-out and cross-validated correlation coefficient q^2 value of 0.511 and the conventional correlation coefficient r^2 value of 0.997. The new compounds with higher activity would be designed from this model. CoMFA model for rat receptor was not successful using all these three alignments, the reason of which maybe that some molecules adopt different conformations for rat receptor.     

两类促肾上腺皮质释放因子(CRF)抑制剂的CoMFA研究

Fipronil and related analogs, a set of new noncompetitive GABAA receptor antagonists, were investigated using comparative molecular field analysis (CoMFA) to explore their three-dimensional quantitative structure-activity relationships (3D-QSAR).Considering the structural complexity of molecules of fipronil and related analogs, three different alignments were performed in this paper. CoMFA model for housefly receptor yield the leave-one-out and cross-validated correlation coefficient q2 value of 0.511 and the conventional correlation coefficient r2 value of 0.997. The new compounds with higher activity would be designed from this model.CoMFA model for rat receptor was not successful using all these three alignments, the reason of which maybe that some molecules adopt different conformations for rat receptor.     

新非核苷喹诺酮类HIV-1逆转录酶抑制剂的CoMFA研究

目的：应用比较分子力场法(COMFA)研究一系列喹诺酮类对HIV-1逆转录酶抑制活性的三维定量构效关系,为进一步抗HIV药物设计提供理论依据。方法和结果：在研究的29个化合物中,用比较分子力场法得到一个CoMFA模型,交叉验证系数为q~2=0.556,具有较高的预测能力及合理性,非交叉验证模型相关系数分别为r~2=0.998,标准偏差SE=0.044,F= 401.038;结论：此模型对设计和预测高活性的喹诺酮类HIV-1逆转录酶抑制活性的化合物有一定可靠性。     

Combining docking, scoring and molecular field analyses to probe influenza neuraminidase-ligand interactions

In this project, several docking conditions, scoring functions and corresponding protein-aligned molecular field analysis (CoMFA) models were evaluated for a diverse set of neuraminidase (NA) inhibitors. To this end, a group of inhibitors were docked into the active site of NA. The docked structures were utilized to construct a corresponding protein-aligned CoMFA models by employing probe-based (H+, OH, CH3) energy grids and genetic partial least squares (G/PLS) statistical analysis. A total of 16 different docking configurations were evaluated, of which some succeeded in producing self-consistent and predictive CoMFA models. However, the best model coincided with docking the ionized ligands into the hydrated form of the binding site via PLP1 scoring function (r2LOO=0.735, r2PRESS against 24 test compounds=0.828). The highest-ranking CoMFA models were employed to probe NA-ligand interactions. Further validation by comparison with a co-crystallized ligand-NA crystallographic structure was performed. This combination of docking/scoring/CoMFA modeling provided interesting insights into the binding of different NA inhibitors.     

黄酮类化合物的3D-QSAR研究

从NCI数据库中,筛选出67个与矢车菊黄素类似的天然黄酮化合物.采用CoMFA方法研究其构效关系,构建CoMFA模型,其模型相关系数为q2=0.599,r2=0.919,验证模型的预测能力和拟合能力较好.通过分子场等势图,可直观分子周围立体和静电特征对化合物活性的影响,为设计高活性黄酮衍生物提供理论依据.     

A CoMFA study of COX-2 inhibitors with receptor based alignment

A diverse set of 53 cyclooxygenase-2 (COX-2) inhibitors which were aligned in two different ways were subjected to CoMFA analysis. The first method of alignment of the molecules was based on the binding information sourced from the crystallographic study, from which CoMFA Model 1 was derived. The second mode of alignment was generated by docking the inhibitors in the binding pocket using the DOCK and AFFINITY suite of programs; this gave a second model. The CoMFA Model 2 was slightly better than Model 1 in terms of the statistical parameters r(2) and q(2). The two models could predict very well the activity of a test set of diverse molecules, with a predictive r(2) of 0.593 and 0.768, respectively. Besides the QSAR results, the docking studies give a deep insight into the H-bonding interactions between the inhibitors and residues in the active site of the enzyme, which can be exploited in designing better inhibitors. Useful ideas on activity improvement could be gleaned from these models.     

3D-QSAR and molecular docking studies of 1,3,5-triazene-2,4-diamine derivatives against r-RNA: novel bacterial translation inhibitors

Aminoglycoside mimetics inhibit bacterial translation by interfering with the ribosomal decoding site. To elucidate the structural properties of these compounds important for antibacterial activity, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were applied to a set of 56 aminoglycosides mimetics. The successful CoMFA model yielded the leave-one-out (LOO) cross-validated correlation coefficient (q(2)) of 0.708 and a non-cross-validated correlation coefficient (r(2)) of 0.967. CoMSIA model gave q(2)=0.556 and r(2)=0.935. The CoMFA and CoMSIA models were validated with 36 test set compounds and showed a good r(pred)(2) of 0.624 and 0.640, respectively. Contour maps of the two QSAR approaches show that electronic effects dominantly determine the binding affinities. These obtained results were agreed well with the experimental observations and docking studies. The results not only lead to a better understanding of structural requirements of bacterial translation inhibitors but also can help in the design of novel bacterial translation inhibitors.     

应用比较分子力场分析法研究氯代苯酚化合物结构与活性的相关性

苯酚是一类对水生生物有严重危害作用的工业化合物。通过比较分子力场分析(CoMFA)法建立标题化合物对发光菌和浮萍活性的三维定量结构-活性关系(3D-QSAR)模型。其中,对发光菌的研究结果为:交叉验证系数q2=0．921,传统的相关系数(非交叉验证系数)R2=0．990。对浮萍的研究结果为:交叉验证系数q2=0．931,传统的相关系数(非交叉验证系数)R2= 0．995。取得优于文献的结果。