新型二芳基三嗪类抗锥体虫病化合物的三维定量构效关系研究

目的 建立具有预测能力的新型二芳基三嗪类抗锥体虫病化合物三维定量构效关系（3D-QSAR）模型。方法 通过对具有抗锥体虫活性的二芳基三嗪类化合物库进行结构分析,利用比较分子场分析法（CoMFA）和比较分子相似性指数分析法（CoMSIA）,建立3D-QSAR模型。结果 模型具有较高q²（q_CoMFA²=0.697,q_CoMSIA²=0.561）和r²（r_CoMFA²=0.998,r_CoMSIA²=0.966）值,表明2组模型具有较高的拟和能力及预测能力。结论 建立的CoMFA和CoMSIA模型均具有良好的预测能力,为设计更高活性的新型二芳基三嗪类抗锥体虫病化合物提供了理论依据和研究方向。     

噻唑-吡唑啉衍生物作为EGFR抑制剂的3D-QSAR和分子对接模型研究

目的 建立EGFR抑制剂结构和活性之间的关系模型,基于对分子活性产生影响的重要结构性因素的信息,设计新的抑制剂分子并预测其活性,为抑制剂分子的设计提供依据。方法 使用Discovery Studio 2019软件进行3D-QSAR的研究以及偏最小二乘的计算;利用Autodock进行分子对接;使用LigPlot研究二维相互作用。结果 模型具有较高的q²(0.521),和r²(r²_training=0.993,r²_test=0.916,r²_blind=0.940),表明模型具有较高的预测能力和拟合能力。结论 预测结果表明,新设计的化合物活性较高,为EGFR抑制剂分子的设计提供了参考。     

噻唑类衍生物二氢乳清酸脱氢酶抑制剂的三维定量构效关系研究

目的 应用三维定量构效关系（3D-QSAR）研究噻唑类衍生物结构的二氢乳清酸脱氢酶抑制活性,为该类药物的设计和筛选提供可靠的理论依据。方法 针对38个以噻唑为基本骨架的二氢乳清酸脱氢酶抑制剂,分别应用分子力场分析（CoMFA）和比较分子相似性指数分析（CoMSIA）2种经典的方法进行了三维定量构效关系（3D-QSAR）研究,建立相关模型,验证模型的预测能力,三维等势图分析噻唑类衍生物结构与活性的关系。结果 CoMFA模型的交叉验证系数q²为0.796,相关系数r²为0.978;CoMSIA模型的q²以及r²分别为0.721和0.976;2种模型对化合物的活性预测与实际值接近;三维等势图可以全面直观的分析化合物结构对其活性的影响。结论 该3D-QSAR模型三维等势图揭示了结构特征与抑制活性的关系,模型具有较好的预测能力和较强的稳定性,为进一步开发研究打下了较好的基础。     

阿片类药物透过血脑屏障的三维构效研究

目的:建立药物透过血脑屏障的三维构效模型,为药物分子设计提供理论依据。方法与结果:利用比较分子力场分析方法建立了阿片类药物透过血脑屏障的三维定量构效模型,该模型有较高的预测能力,交叉验证系数r²_cv=0.718,相关系数r²=0.978,F₃,7=67.902,标准偏差SE=0.209。结论:根据CoMFA模型系数等势图,解释了该类药物透过血脑屏障的构效关系。     

噻嗪类11β-羟类固醇脱氢酶抑制药三维定量构效关系研究

摘 要 目的：通过构建噻嗪类11β-羟类固醇脱氢酶（11β-HSD）抑制药的三维定量构效关系(3D QSAR)模型,用于其结构改造及发现具有更高生物活性的11β-HSD抑制药。方法: 基于骨架叠合的模式,采用比较分子力场分析（CoMFA）的方法,构建噻嗪类衍生物定量构效关系模型,并用分子对接的方法对已构建的3D QSAR模型进行验证。结果: 得到了高精度的11β-HSD抑制药的3D-QSAR模型（CoMFA:q²=0.346,r²=0.850;其中q²为交叉验证系数,r²为非交叉验证系数）。结论:本文构建的3D QSAR模型可为11β-HSD抑制药骨架各个位点的化学修饰,实施定向合理设计,为开发新型抗2型糖尿病药物提供理论基础。     

三维全息原子场作用矢量用于四氢-咪唑-苯二氮酮类抗艾滋病药物定量构效关系研究

目的对23个四氢-咪唑-苯二氮酮(TIBO)类抗艾滋病药物分子进行定量构效关系(QSAR)研究。方法采用本实验室新近提出的三维全息原子场作用矢量(3D-HoVAIF)表征TIBO类抗艾滋病药物分子结构。然后运用偏最小二乘回归(partial least square regression，PLS)建立3D-HoVAIF描述符与TIBO类抗艾滋病药物活性之间的QSAR模型。结果用此方法建模的复相关系数(r^2cum)、交互校验复相关系数(q^{2cum)和模型的标准偏差(SD)分别为r2cum=0.824，q2_cum=0.778与SD=0.56，均优于文献值。结论3D-HoVAIF能较好表征TIBO类抗艾滋病药物分子结构信息，因而能建立具有良好稳定性和预测能力的QSAR模型。}     

维甲类化合物构效关系研究:抗致癌活性的三维构效关系

使用比较分子场分析法(comparativemolecularfieldanalysis,CoMFA)[1]建立了一个维甲类化合物抗致癌活性的三维构效关系(3D-QSAR)模型,用交叉验证法(cros-validation)、和非交叉验证方法(non-cros-validation)分别验证和建立的分子场模型,高的交叉验证回归系数(R2CV=0.905)说明系列化合物分子周围空间场和静电场分布的差异与其生物活性差异间存在良好的相关性。用这个模型预测在建立模型时没有包括进去的三个化合物的活性,预测值接近实验值,提示该模型有较好的活性预测能力,可用来指导设计新的高活性维甲分子。化合物分子的最低能量构象未必是其活性构象。用得到的化合物最低能量构象进行CoMFA研究,其交叉验证系数较低(R²_CV=0.420),不具有统计学意义。但将部分分子的单键微弱旋转,构象能变化控制在2kcal·mol^-1之内,其它分子采取最低能量构象,则得到高的交叉验证系数。叠合的配体分子场模拟了配体分子周围的作用环境,反映受体结合部位与配体之间存在相互作用的基团和(或)原子的空间和静电性质,分子场模型在作为预测活性模板的同时也在一定程度上映射出受体结合部位的三维拓扑形状和理化特性。     

药物透过血脑屏障的3D-QSPR:H2受体拮抗剂的CoMFA和EVA分析

目的：药物透过血脑屏障是药代动力学的重要过程,H₂受体拮抗剂是作用于神经外周的抗溃疡药物,为避免该类药物透过血脑屏障损伤中枢神经,产生毒副作用,指导该类药物的设计与合成。方法和结果：选择了不依赖于实验参数的比较分子力场分析(CoMFA)方法和最近发展的本征值(EVA)方法,建立了有关的三维药代动力学性质(3D-QSPR)模型。CoMFA模型的统计参数为：交叉验证系数r²_cv=0.625,相关系数r2=0.893,F_3,17=47.270,标准偏差SE=0.254;EVA模型的统计参数为：交叉验证系数r²_cv=0.697,相关系数r²=0.922,F_3,17=67.766,标准偏差SE=0.203。结论：两种方法都能建立三维定量构效模型,EVA模型有更高的预测能力。     

3-甲基芬太尼衍生物立体异构体的 QSAR 研究

用比较分子力场分析(CoMFA)方法研究了3-甲基芬太尼和羟甲芬太尼立体异构体的三维定量构效关系(3D-QSAR)。所得CoMFA-QSAR模型有很好的预测能力(γ²_{cros-validated}=0.716,n_{optimalcomponent}=5,γ²_conventional=0.999,s=0.052,F=1305.1),模型中,被研究化合物的构象可能就是其活性构象。以AM1方法进行量子化学计算,获得上述可能活性构象的结构参数及空间位置参数。基于这些参数,用偏最小二乘法(PLS)获得了被研究化合物的QSAR方程。所得PLS-QSAR模型具有较好的预测能力,并且显示被研究化合物的镇痛活性取决于分子中负电性的哌啶氮原子(N_PA)净电荷以及哌啶氮原子、羰基氧原子、1-β-苯环、4-N-苯环、3-甲基和2′-羟基的空间位置。     

魔芋葡甘聚糖分子链形态及链参数研究魔芋葡甘聚糖分子链形态及链参数研究

目的研究魔芋葡甘聚糖分子链形态，测定分子链参数。方法采用光散射、凝胶渗透色谱及粘度法测定溶液行为；采用原子力显微镜及透射电镜直接观测分子形貌。结果魔芋葡甘聚糖重均分子量M_w、均方根旋转半径〈S₂〉^1/2以及第二维利系数A₂分别为1.04×10⁶, (105.0±0.9) nm,和 (-1.59±0.28)×10^-3 mol·mL·g^-2，多分散系数M_w/M_n为1.02，Mark-Houwink方程为［η］=5.96×10^-2M^0.73_w；分子链参数M_L=982.82 nm^-1,q=27.93 nm, d=0.74 nm,h=0.26 nm,L=1 054.11 nm。结论直接观察到的分子形貌和根据溶液行为的推测均说明，魔芋葡甘聚糖为伸展的有一定刚性的半屈曲性直链分子，不存在支链。     

Molecular docking and 3D-QSAR studies on checkpoint kinase 1 inhibitors

Checkpoint kinase 1(Chk1) is a promising target for cancer treatment. Here three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on 174 1,4-dihydroindeno[1,2-c]pyrazole inhibitors of Chk1 using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Two satisfactory ligand-based QSAR models were built (CoMFA model: q ² = 0.541, r ² = 0.880, CoMSIA model: q ² = 0.590, r ² = 0.902). The docking-based studies presented a detailed understanding of interaction between the inhibitors and Chk1. The obtained QSAR models are highly predictable (CoMFA model: q ² = 0.567, r ² = 0.891, CoMSIA model: q ² = 0.596, r ² = 0.917). The models were further validated by an external testing set obtaining $ r_{\text{pred}}^{2} $ r pred 2 values 0.896 and 0.923 for CoMFA and CoMSIA, respectively. So our models might be helpful for further modification of 1,4-dihydroindeno[1,2-c]pyrazole derivatives.     

A combined 3D QSAR and pharmacophore-based virtual screening for the identification of potent p38 MAP kinase inhibitors: an in silico approach

p38 kinase plays a vital role in inflammation mediated by tumor necrosis factor-α and interleukin-1β pathways. Inhibition of p38 kinase provides an effective way to treat inflammatory diseases. 3D-QSAR study was performed to obtain reliable comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for a series of p38 inhibitors with three different alignment methods (Receptor based, atom by atom matching, and pharmacophore based). Among the different alignment methods, better statistics were obtained with receptor-based alignment (CoMFA: q ² = 0.777, r ² = 0.958; CoMSIA: q ² = 0.782, r ² = 0.927). Superposing CoMFA/CoMSIA contour maps on the p38 active site gave a valuable insight to understand physical factors which are important for binding. In addition, this pharmacophore model was used as a 3D query for virtual screening against NCI database. The hit compounds were further filtered by docking and scoring, and their biological activities were predicted by CoMFA and CoMSIA models.     

Pharmacophore-based 3D-QSAR of HIF-1 inhibitors

(Aryloxyamino)benzoic acids and nicotinic/isonicotinic acids represent an important new class of small molecules that inhibit the activation of Hypoxia-Inducible Factor (HIF)-1. In order to understand the factors affecting inhibitory potency of HIF-1 inhibitors, 3 dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed. Since no receptor structure are available, the pharmacophore-based alignment was used for comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The CoMFA and CoMSIA models gave reasonable statistics (CoMFA: q² = 0.564, r²=0.945; CoMSIA: q² = 0.575, r²=0.929). Both CoMFA and CoMSIA results indicate that the steric interaction is a major factor, while CoMSIA suggests importance of hydrogen bonding. These findings about steric and H-bonding effects can be useful to design new inhibitors. Equally contributed in this work.     

Investigations and design of pyridine-2-carboxylic acid thiazol-2-ylamide analogs as methionine aminopeptidase inhibitors using 3D-QSAR and molecular docking

Methionine amino peptidases (MetAPs) are metalloproteases that remove co-translational N-terminal methionine from nascent polypeptide chains. Due to their essential role in protein synthesis, MetAPs are considered as potential targets for antibacterial drugs. In the present work, three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were carried out on a series of pyridine-2-carboxylic acid thiazol-2-ylamide-based MetAP inhibitors using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. The models were developed using 30 training set molecules. The optimum CoMFA and CoMSIA models obtained for the training set were statistically significant with cross-validated correlation coefficients (q ²) of 0.799 and 0.704 and conventional correlation coefficients (r ²) of 0.989 and 0.954, respectively. These inhibitors were docked into MetAP active site. The CoMFA and CoMSIA field contour maps correlate well with the structural characteristics of the binding pocket of MetAP active site. Using the knowledge of structure–activity relationship and receptor–ligand interactions from 3D-QSAR model and the docked complexes, four new pyridine-2-carboxylic acid thiazol-2-ylamide analogs were designed. These analogs exhibit significantly better predicted activity than the reported molecules. The present work has implications for the development of novel antibiotics as potent MetAP inhibitors.     

3D-QSAR study of pyrrolidine derivatives as matrix metalloproteinase-2 inhibitors

In order to develop potent inhibitors of matrix metalloproteinase-2(MMP-2) as anticancer agents, a three-dimensional quantitative structure–activity relationship (3D-QSAR) model was established by using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. This study correlates the MMP-2 inhibitory activities of 67 pyrrolidine derivatives to steric, electrostatic, hydrophobic, and hydrogen-bond donor and acceptor fields. After using two different molecular alignments, both CoMFA and CoMSIA models resulted in good statistical predictions, a case in point being their high q ² values of between 0.757 and 0.843. The CoMFA and CoMSIA models established herein will be helpful in understanding the structure–activity relationship of pyrrolidine derivatives as well as in the design of novel derivatives with enhanced MMP-2 inhibitory activity.     

3D-QSAR studies of chromone derivatives as iron-chelating agent

The metal-chelating activity of a series of 48 chromone compounds, evaluated by ferrous (Fe²⁺) chelating test, were subjected to 3D-QSAR studies using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The best CoMFA model obtained from HF/6-31G* geometry optimization and field fit alignment gave cross-validated r ² (q ²) = 0.582, non-cross-validated r ² = 0.975. The best CoMSIA model gave q ² = 0.617, non-cross-validated r ² = 0.917. The resulted CoMFA and CoMSIA contour maps proposed the Fe²⁺-chelating sites of chromone compounds compared with those of quercetin.     

Predictive 3D-QSAR and HQSAR model generation of isocitrate lyase (ICL) inhibitors by various alignment methods combined with docking study

Isocitrate lyase (ICL) is one of the most important targets in the treatment of Mycobacterium tuberculosis. In this study a diverse set of 2-benzanilide derivatives were aligned by two different methods for CoMFA, CoMSIA, and HQSAR analysis. The best CoMFA model was obtained with the internal validation value (q ²) of 0.730 and conventional coefficient (r ²) of 0.944. Various CoMSIA models were generated and cross-validated. The best cross-validation coefficient (q ²) value was found to be statistically satisfactory (0.688). Both the models were validated by test set of 10 compounds with satisfactory prediction value of (r ² _pred ) 0.725 and 0.631 for CoMFA and CoMSIA, respectively. Cross-validation coefficient value (q ²) of 0.694 and r ² of 0.856 were obtained for HQSAR study. The docking study reveals that large hydrophobic pockets occupy R substitutions of these compounds. An electronically negative surface is observed near R₁ substitution. The results of the 3D-QSAR analysis corroborate with the molecular docking results, and our findings will serve as a basis for further development of better allosteric inhibitors of ICL inhibitors against M. tuberculosis.