QSAR and Pharmacophore Studies of Thiazolidine-4-carboxylic Acid Derivatives as Novel Influenza Neuraminidase Inhibitors Using HQSAR, Topomer CoMFA and CoMSIA

In order to understand the chemical-biological interactions governing their activities toward neuraminidase (NA), QSAR models of 28 thiazolidine-4-carboxylic acid derivatives with inhibitory influenza A virus were developed. The obtained HQSAR (hologram quantitative structure activity relationship), Topomer CoMFA and CoMSIA (comparative molecular similarity indices analysis) models were robust and had good exterior predictive capabilities. Moreover, QSAR modeling results elucidated that hydrogen bonds highly contributed to the inhibitory activity, then electrostatic and hydrophobic factors. Squared multiple correlation coefficients (R2) of HQSAR, Topomer CoMFA and CoMSIA models were 0.994, 0.978 and 0.996, respectively. Squared cross-validated correlation coefficients (Q2) of HQSAR, Topomer CoMFA and CoMSIA models were in turn 0.951, 919 and 0.820. Furthermore, squared multiple correlation coefficients for the test set (R2test) of HQSAR, CoMFA and CoMSIA models were 0.879, 0.912 and 0.953, respectively. Squared cross-validated correlation coefficients for the test set (Q2ext) of HQSAR, Topomer CoMFA and CoMSIA models were 0.867, 0.884 and 0.899, correspondingly.     

三苯胺类染料敏化太阳能电池能量转化效率的全息定量构效关系研究

研究构建了三苯胺类化合物分子结构与相应染料敏化太阳能电池能量转化效率(PCE)之间的全息定量构效关系(HQSAR)模型。当fragment distinction、fragment size、hologram length和principal components分别为“C、DA”、“4-7”、“199”和“6”时,可以获得最优HQSAR模型。采用外部测试集验证和留一交叉验证对所建立模型进行检验,外部测试集验证中CCC和 Q2F3分别为0.933和0.892,留一交叉验证中其q2cv和r2分别为0.791和0.902,表明所建立模型具有较好的拟合效果和预测能力。通过所建立HQSAR模型的分子贡献图可知,环戊并二噻吩基团的存在有利于提高PCE值,长烷基链的存在可能降低PCE值。     

Insight into the structural requirements of narlaprevir-type inhibitors of NS3/NS4A protease based on HQSAR and molecular field analyses

In the life cycle of hepatitis C virus (HCV), NS3/NS4A protease has been proved to play a vital role in the replication of the HCV virus. Narlaprevir and its derivatives, the inhibitors of NS3/NS4A, would be potentially developed as important anti-HCV drugs in the future. In this study, quantitative structure-activity relationship (QSAR) analyses for 190 narlaprevir derivatives were conducted using comparative molecular field analysis (CoMFA), comparative molecular indices analysis (CoMSIA) and hologram quantitative structure-activity relationship (HQSAR) techniques. Both of the best CoMFA and HQSAR models showed statistical significance for the training set and good predictive accuracy for the test set, which strongly manifested the robustness of the CoMFA and HQSAR models. The CoMFA contour maps and the HQSAR contribution maps were both presented. Furthermore, based on the essential factors for ligand binding derived from the QSAR models, sixteen new derivatives were designed and some of them showed higher inhibitory activities confirmed by our models and molecular docking studies. General speaking, this study provides useful suggestions for the design of potential anti-HCV drugs.     

Topomer CoMFA,HQSAR Studies and Molecular Docking of 2,5-Diketopiperazine Derivatives as Oxytocin Inhibitors

Topomer comparative molecular field analysis(Topomer Co MFA) and holographic quantitative structure-activity relationship(HQSAR) for 130 2,5-diketopiperazine derivatives were used to build a three-dimensional quantitative structure-activity relationship(3D-QSAR) model. The results show that the models have high predictive ability. For Topomer CoMFA, the cross-validated q~2 value is 0.710 and the non-cross-validated r~2 value is 0.834. The most effective HQSAR model shows that the cross-validation q~2 value is 0.700, the non-cross-validated r~2 value is 0.815, and the best hologram length value is 353 using connections and bonds as fragment distinctions. 50 highly active 2,5-diketopiperazine derivatives were designed based on the three-dimensional equipotential map and HQSAR color code map. Finally, the molecular docking method was also used to study the interactions of these new molecules by docking the ligands into the diketopiperazine active site, which revealed the likely bioactive conformations. This study showed that there are extensive interactions between the new molecule and Arg156, Arg122 residues in the active site of diketopiperazine. These results provide useful insights for the design of potent of the new 2,5-diketopiperazine derivatives.     

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.     

Predicting toxicity of benzene derivatives by molecular hologram derived quantitative structure-activity relationships (QSARS)

Holographic quantitative structure-activity relationship (HQSAR) is an emerging QSAR technique with the combined application of molecular hologram, which encodes the frequency of occurrence of various molecular fragment types, and the subsequent partial least squares (PLS) regression analysis. Based on molecular hologram, alignment-free QSAR models could be rapidly and easily developed with highly statistical significance and predictive ability. In this paper, the toxicity data for a series of 83 benzene derivatives to the autotrophic Chlorella vulgaris (IGC50, negative logarithmic form of 6-h 50% population growth inhibition concentration in mmol/l) were subjected to HQSAR analysis and this resulted in a model with a high predictive ability. The robustness and predictive ability of the model were validated by "leave-one-out" (LOO) cross-validation procedure and an external testing set. The influence of fragment distinction parameters and fragment size on the quality of the HQSAR model have been also discussed.     

5,6-二氢-(9H)-吡唑[3,4-c]-1,2,4-三唑[4,3-a]吡啶类抑制剂的HQSAR研究

应用分子全息定量构效关系(HQSAR)分析方法,以5,6-二氢-(9H)-吡唑[3,4-c]-1,2,4-三唑[4,3-a]吡啶类抑制剂为研究对象,建立了一组对磷酸二酯酶4有抑制活性的化合物HQSAR模型,分析化合物活性与分子结构之间的关系.探讨了分子全息长度、分子碎片大小以及碎片区分参数对模型质量的影响.最优模型的交叉验证相关系数q2=0.628,非交叉验证相关系数r2=0.930,标准偏差SE=0.277.该模型具有较好的预测能力,对该类化合物性质的预测及进一步合成工作有指导意义.     

Improved CoMFA modeling by optimization of settings

The possibility of improving the predictive ability of comparative molecular field analysis (CoMFA) by settings optimization has been evaluated to show that CoMFA predictive ability can be improved. Ten different CoMFA settings are evaluated, producing a total of 6120 models. This method has been applied to nine different data sets, including the widely used benchmark steroid data set, as well as eight other data sets proposed as QSAR benchmarking data sets by Sutherland et al. (J. Med. Chem. 2004, 47, 5541-5554). All data sets have been studied using training and test sets to allow for both internal (q(2)) and external (r(2)(pred)) predictive ability assessment. CoMFA settings optimization was successful in developing models with improved q(2) and r(2)(pred) as compared to default CoMFA modeling. Optimized CoMFA is compared with comparative molecular similarity indices analysis (CoMSIA) and holographic quantitative structure-activity relationship (HQSAR) models and found to consistently produce models with improved or equivalent q(2) and r(2)(pred). The ability of settings optimization to improve model predictive ability has been validated using both internal and external predictions, and the risk of chance correlation has been evaluated using response variable randomization tests.     

Predicting toxicity of benzene derivatives by molecular hologram derived quantitative structure–activity relationships (QSARS)

Holographic quantitative structure–activity relationship (HQSAR) is an emerging QSAR technique with the combined application of molecular hologram, which encodes the frequency of occurrence of various molecular fragment types, and the subsequent partial least squares (PLS) regression analysis. Based on molecular hologram, alignment-free QSAR models could be rapidly and easily developed with highly statistical significance and predictive ability. In this paper, the toxicity data for a series of 83 benzene derivatives to the autotrophic Chlorella vulgaris (IGC⁵⁰, negative logarithmic form of 6-h 50% population growth inhibition concentration in mmol/l) were subjected to HQSAR analysis and this resulted in a model with a high predictive ability. The robustness and predictive ability of the model were validated by “leave-one-out” (LOO) cross-validation procedure and an external testing set. The influence of fragment distinction parameters and fragment size on the quality of the HQSAR model have been also discussed.     

基于5-芳基乙内酰脲类化合物的CoMFA和HQSAR研究

用CoMFA和HQSAR两种QSAR方法研究了50个乙内酰脲类分子的定量构效关系.本研究从构象搜索所得的低能结构出发构建化合物分子的构象, 建立CoMFA模型,并进行了全空间搜索. HQSAR本质上是一种二维的QSAR方法,与CoMFA方法相比,该方法在数据处理方面,比CoMFA方法快捷,并且可重复性好.两种方法均得到了较好分析结果, CoMFA的交叉验证相关系数q2 值为0.815, HQSAR的q2值为0.893.这些方程有力地说明了该类分子在(R,R)-N-3,5-dinitrobenzoyl-1,2-diamine型手性固定相上拆分过程中的影响因素,对今后类似拆分的实验研究提供了理论支持.     

Quantitative structure-activity relationships for a series of selective estrogen receptor-beta modulators

The estrogen receptor-beta subtype (ERbeta) is an attractive drug target for the development of novel therapeutic agents for hormone replacement therapy. Hologram quantitative structure-activity relationships (HQSAR) were conducted on a series of 6-phenylnaphthalene and 2-phenylquinoline derivatives, employing values of ERbeta binding affinity. A training set of 65 compounds served to derive the models. The best statistical HQSAR model (q(2) = 0.73 and r(2) = 0.91) was generated using atoms, bonds, connections and donor and acceptor as fragment distinction parameters, and fragment size default (4-7) with hologram length of 199. The model was used to predict the binding affinity of an external test set of 16 compounds, and the predicted values were in good agreement with the experimental results. The final HQSAR model and the information obtained from 2D contribution maps should be useful for the design of novel ERbeta modulators having improved affinity.     

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.     

3-吡啶基醚类化合物的分子全息QSAR研究

采用分子全息定量构效关系(HQSAR, hologram quantitative structure-activity relationship)方法, 研究了28个3-吡啶基醚类化合物对乙酰胆碱α4β2受体的亲和性与它们的分子结构之间的关系, 讨论了分子碎片大小、分子碎片亚结构类型以及分子全息长度对QSAR的影响, 得到了较好的HQSAR模型, 模型的交叉验证系数平方q²＝0.670, 非交叉相关系数平方r²＝0.965, 偏差S＝0.093. 利用HQSAR的颜色编码, 对化合物中不同基团对亲和活性的影响进行了讨论, 对新配体的合成具有一定的指导作用.     

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.     

苯并咪唑类缓蚀剂的HQSAR研究及分子设计

采用分子全息定量构效关系(HQSAR)方法, 构建苯并咪唑衍生物在酸性环境中的缓蚀性能与结构之间的定量构效关系模型, 研究不同碎片区分参数及碎片大小对模型质量的影响, 寻找最优HQSAR模型, 并对其稳定性及预测能力进行评价. 结果显示: 选取碎片区分参数为原子类型(A)、化学键类型(B)、连接性(C)、氢原子(H)、手性(Ch)、氢键给体和受体(D&;A), 碎片大小为1-3 建模时, 得到的HQSAR 模型(r²(非交叉验证系数)=0.996, q²(交叉验证系数)=0.960, SE_cv(交叉验证标准误差)=3.709)具有良好的统计学稳定性及预测能力. 根据最优HQSAR模型图设计出的38种苯并咪唑类化合物理论上均具有较好的缓蚀性能. 本研究为油气田新型高效缓蚀剂研发提供可靠的理论依据.     

3D CoMFA,CoMSIA, topomer CoMFA and HQSAR studies on aromatic acid esters for carbonic anhydrase inhibitory activity

Molecular modelling studies [comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), topomer CoMFA and hologram quantitative structure–activity relationship (HQSAR)] have been performed on the series of 28 molecules belonging to the series of aromatic acid ester derivatives for their carbonic anhydrase inhibitory activity. The model exhibited good correlation coefficient (r²) and cross‐validated correlation coefficient (q²) for CoMFA, CoMSIA and HQSAR methods. On the basis of the findings from all these studies, a structure–activity relationship was established. Copyright © 2013 John Wiley & Sons, Ltd.     

3D QSAR and molecular docking studies of benzimidazole derivatives as hepatitis C virus NS5B polymerase inhibitors

The urgent need for novel HCV antiviral agents has provided an impetus for understanding the structural requisites of NS5B polymerase inhibitors at the molecular level. Toward this objective, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) of 67 HCV NS5B polymerase inhibitors were performed using two methods. First, ligand-based 3D QSAR studies were performed based on the lowest energy conformations employing the atom fit alignment method. Second, receptor-based 3D QSAR models were derived from the predicted binding conformations obtained by docking all NS5B inhibitors at the allosteric binding site of NS5B (PDB ID: 2dxs). Results generated from the ligand-based model were found superior (r2cv values of 0.630 for CoMFA and 0.668 for CoMSIA) to those obtained by the receptor-based model (r2cv values of 0.536 and 0.561 for CoMFA and CoMSIA, respectively). The predictive ability of the models was validated using a structurally diversified test set of 22 compounds that had not been included in a preliminary training set of 45 compounds. The predictive r2 values for the ligand-based CoMFA and CoMSIA models were 0.734 and 0.800, respectively, while the corresponding predictive r2 values for the receptor-based CoMFA and CoMSIA models were 0.538 and 0.639, respectively. The greater potency of the tryptophan derivatives over that of the tyrosine derivatives was interpreted based on CoMFA steric and electrostatic contour maps. The CoMSIA results revealed that for a NS5B inhibitor to have appreciable inhibitory activity it requires hydrogen bond donor and acceptor groups at the 5-position of the indole ring and an R substituent at the chiral carbon, respectively. Interpretation of the CoMFA and CoMSIA contour maps in context of the topology of the allosteric binding site of NS5B provided insight into NS5B-inhibitor interactions. Taken together, the present 3D QSAR models were found to accurately predict the HCV NS5B polymerase inhibitory activity of structurally diverse test set compounds and to yield reliable clues for further optimization of the benzimidazole derivatives in the data set.     

Three-dimensional quantitative structure--property relationship (3D-QSPR) models for prediction of thermodynamic properties of polychlorinated biphenyls (PCBs): enthalpy of sublimation

Three-dimensional quantitative structure--property relationship (3D-QSPR) models have been constructed using comparative molecular field analysis (CoMFA) to correlate the sublimation enthalpies at 298.15 K of a series of polychlorinated biphenyls (PCBs) with their CoMFA-calculated physicochemical properties. Various alignment schemes, such as atom fit, as is, and inertial were employed in this study. Separate CoMFA models were developed using different partial charge formalisms, namely, electrostatic potential (ESP) and Gasteiger-Marsili (GM) charges. Among the four different CoMFA models constructed for sublimation enthalpy (Delta(sub)H(m)(298.15 K)), the model that combined atom fit alignment and ESP charges yielded the greatest self-consistency (r(2) = 0.976) and internal predictive ability (r(cv)(2) = 0.750). This CoMFA model was used to predict Delta(sub)H(m)(298.15 K) of PCBs for which the corresponding experimental values are unavailable in the literature.