R基团搜索技术用于硼酸三肽蛋白酶体抑制剂的分子设计

本文采用Topomer CoMFA对44个Tyropeptin硼酸三肽类蛋白酶体抑制剂进行三维定量构效关系(3D-QSAR)分析。所得最优模型的拟合、交互验证、及外部验证的复相关系数分别为0.983、0.651、0.963。采用Topomer search对ZINC数据库进行R基团的虚拟筛选,得到具有特定活性贡献的R基团,以活性最高的分子为模板过滤,得到7个R1和5个R2基团。并以此设计得到活性优于模板分子的20个新化合物。结果表明,所建立的Topomer CoMFA模型具有良好的稳定性和预测能力,基于R集团的Topomer search技术可以有效筛选,并为设计出新的蛋白酶体抑制剂提供理论依据。     

R基团搜索技术用于PTH类Tau蛋白抑制剂的分子设计

对26个PTH类Tau蛋白抑制剂进行了Topomer CoMFA研究, 建立了拟合及预测能力良好的Topomer CoMFA模型, 获得的模型拟合、 交互验证及外部预测的复相关系数分别为0.976, 0.603和0.795, 估计标准偏差和Fisher验证值F分别为0.110和115.778. 使用ZINC化合物数据集作为结构片段源, 通过三维定量构效关系(3D-QSAR)模型搜索具有特定活性贡献的R基团. 以样本中活性最高的1号分子过滤, R₁和R₂贡献值均提高了20%的片段分别有9个与2个. 以此交替取代1号样本的R₁与R₂, 得到18个新颖化合物并预测其活性, 其中的15个预测活性值优于模板分子. 研究结果表明, Topomer search可有效地用于分子设计, 所设计的分子为阿尔茨海默病(AD)药物的研发提供了新的候选物.     

分子对接技术用于马来酰亚胺类GSK-3α抑制剂的作用特征分析

糖原合酶激酶-3α(GSK-3α)是治疗阿尔兹海默症(AD)的关键靶点之一.采用基于R基团的搜索组合分子对接研究了GSK-3α抑制剂的作用特征.以45个马来酰亚胺类GSK-3α抑制剂分子为训练集,采用Topomer CoMFA建立3D-QSAR模型,其拟合与留一法交互验证的复相关系数和标准差分别为r2=0.797,SD=0.210,q2cv=0.611,SDcv=0.280,对22个测试集样本外部预测的复相关系数与标准差分别为r2pred=0.703,SDpred=0.213.以Topomer Search搜索技术设计了25个理论上具有更高活性的新型分子.分子对接对比研究表明,新设计的分子与建模样本同GSK-3α的作用位点具有类似的作用特征,且与对比文献一致.该研究为AD治疗的分子设计与研发提供了新的思路.     

R基团筛选技术用于吡啶杂环类mTOR抑制剂的分子设计研究

针对27个吡啶杂环类抑制剂采用Topomer COMFA方法进行了三维定量构效关系分析,新建模型的拟合、交互验证及外部验证的复相关系数分别为r2=0.982,q2=0.857,r2pred=0.829,结果表明模型具有良好的预测能力和可信度.采用基于R基团搜索Topomer Search技术对ZINC数据库进行R基团的虚拟筛选,获得了6个高活性的新抑制剂分子,其预测活性均优于训练集中活性最高分子.运用Surflex-dock分子对接法研究吡啶杂环类抑制剂与mTOR靶点的作用模式.研究结果表明,Topomer search可有效地用于分子设计,结合分子对接结果,新抑制剂分子为mTOR靶向药物设计提供参考.     

基于Topomer CoMFA方法的喹诺酮羧酸类衍生物的QSAR研究及分子设计

采用基于R基团搜索技术的Topomer Co MFA技术对一系列喹诺酮羧酸类衍生物进行三维定量构效(3D-QSAR)关系研究,所得模型结果的交叉验证相关系数(q2)为0.790,非交互验证系数(r2)为0.890,外部验证的复相关系数(r2pred)为0.878,研究结果表明该模型具有良好的稳定性和预测能力。采用Topomer search技术在ZINC数据库中进行虚拟筛选,筛选出6个Ra基团和3个Rb基团,进而设计出12个具有更高活性的新型喹诺酮羧酸类化合物。采用分子对接技术对药物与受体的作用机制进行了研究,结果显示,药物与蛋白酶的ASP 30、ASP 29和ASN 25位点作用明显,该QSAR的研究结果可为新药合成提供理论参考。     

基于Topomer CoMFA和Surflex-dock的GSK-3β抑制剂的3D-QSAR与作用模式研究

GSK-3β的过度表达可导致人脑神经细胞内Tau蛋白的过磷酸化,从而介导阿尔兹海默病(Alzheimer’s disease,AD)的发生.本文旨在研究GSK-3β的马来酰胺类抑制剂的三维定量构效关系(3D-QSAR)及新抑制剂分子与GSK-3β的作用机制.采用基于R基团搜索技术的Topomer CoMFA建立了49个马来酰胺类GSK-3β抑制剂的3D-QSAR模型,并用包括25个样本的测试集验证模型的外部预测能力.所得优化模型的拟合、交互验证以及外部验证的复相关系数分别为0.928,0.790和0.725.采用Topomer search在ZINC分子数据库中进行虚拟搜索,设计了28个可能具有更高活性的新抑制剂.借助Surflex-dock分子对接研究了新抑制剂与GSK-3β作用模式与机制.结果显示,新抑制剂与GSK-3β的Asp133,Tyr134,Val135和Pro136等位点作用显著.     

类黄酮抑制 IP6Ks 的3D-QSAR 研究

采用基于R基团搜索技术的Topomer CoMFA建立了14个类黄酮类肌醇六磷酸激酶抑制剂的3D-QSAR模型,研究了类黄酮化合物对肌醇六磷酸激酶(IP6Ks)活性的抑制作用.该模型的主成分数为3,拟合与留一法交互验证的复相关系数以及F检验值分别为q~2=0.842,q_(st)~2=0.26;r~2=0.965,r_(st)~2=0.12;F=91.519.在此基础上通过Topomer Search进行分子片段筛选,对化合物8,14和6进行重新拼接设计,其预测活性可以分别提高12.76倍、9.27倍和62%.运用Surflex-dock分子对接法研究了实验数据中活性最高的化合物6和活性最低的化合物10与IP6Ks的PDB结构的作用机制,发现并验证了之前所建立的Topomer CoMFA模型构效关系分析研究的结果,进一步阐明了化合物6抑制活性更高的原因.结果表明,在类黄酮分子结构的C(5),C(7)和C(4′)位上,取代基团的大小和静电性质对其抑制活性产生重要的影响.本研究可能对以天然产物设计和合成具有更好生物活性的IP6Ks抑制剂具有指导作用.     

基于配体的组蛋白去乙酰化酶抑制剂的分子设计

本文采用Topomer Co MFA方法对39个组蛋白去乙酰化酶抑制剂进行了3D-QSAR研究,得到q~2=0. 877,r~2=0. 987的可靠模型。运用基于R基团搜索的Topomer Search技术对ZINC2015数据库进行了虚拟筛选,筛选出一批具有潜在活性的目标化合物,模型预测结果表明,筛选出的化合物活性比最初合成的化合物大幅度提高,其中筛选出的最高活性化合物S2-7(IC_(50)=0. 0235μmol·L~(-1))活性达到了最初合成的高活性化合物21(IC_(50)=0. 103μmol·L~(-1))的4倍。分子对接技术揭示了化合物结构和靶酶之间的联系,为更新型HDACIs的设计以及结构优化提供了重要信息和理论指导。     

烟酰胺类琥珀酸脱氢酶抑制剂的构效关系研究和虚拟筛选

琥珀酸脱氢酶作为真菌线粒体呼吸链的靶点已成为研究热点,本文采用Topomer CoMFA方法对52个训练集和10个测试集的琥珀酸脱氢酶抑制剂进行了3D-QSAR研究,得到q~2=0.724,r~2=0.859的可靠模型。运用Topomer Search技术对ZINC2015数据库中进行了虚拟筛选,得到了10个具有较高预测活性的苗头化合物。其中2-氯-N-(2-(3-甲基吡咯烷~(-1)-基)吡啶~(-3)-基)烟酰胺(化合物S09)具有最高的抑菌活性的。该模型具有较好的预测能力,能够为新型琥珀酸脱氢酶抑制剂的设计和结构修饰提供重要信息。     

HIV-1逆转录酶抑制剂的三维定量构效关系

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)对一系列非核苷类HIV-1逆转录酶抑制剂(苯磺酰基亚胺噻唑类化合物)进行了三维定量构效关系的研究,获得了高可靠性的CoMFA和CoMSIA模型,其交叉验证相关系数q2值分别为0.748和0.607.通过对CoMFA和CoMSIA模型三维等势图的分析,确定了该类化合物抗HIV-1活性的结构要求.研究结果表明,对苯磺酰基亚胺噻唑类化合物而言,在苯环的C-5位引入体积大和电负性强的基团能增加其抑制活性;苯环的C-2位的氢键给体基团对活性有利;噻唑环的R2取代基疏水性增大会降低生物活性.研究结果表明,可以指导新HIV-1逆转录酶抑制剂的设计和合成.     

磺酰脲类乙酰羟基酸合成酶抑制剂Topomer CoMFA研究

磺酰脲类除草剂是一类高选择性、广谱、低毒的化合物,在世界范围内得到了广泛的应用。本文采用易位体-比较分子力场法（Topomer CoMFA）对75个磺酰脲类化合物与植物源野生型拟南芥AHAS酶的离体相互作用进行了三维定量构效关系研究,快速准确地构建了Topomer CoMFA模型,该模型具有较强的预测能力（交叉验证相关系数q2为0.890,非交叉验证相关系数r2为0.967）。此模型对测试集的10个化合物的pKi值进行预测,其预测值与实际值一致。     

类黄酮抑制P糖蛋白的三维定量构效关系与作用模式

采用基于R基团搜索技术的Topomer CoMFA建立了30个类黄酮类P糖蛋白抑制剂的三维定量构效关系(3D-QSAR)模型, 并用包括9个样本的测试集验证模型的外部预测能力. 所得模型的拟合、 交互验证以及外部验证的复相关系数分别为r²=0.971, q²=0.728和\begin{array}{c}{r}_{\mathit{pred}}^2\end{array}=0.816. 在此基础上, 运用Surflex-dock分子对接法研究了白杨素及其异戊烯化衍生物与P糖蛋白的作用模式. 结果表明, 异戊烯化修饰可显著提高类黄酮的亲脂性, 修饰产物能更好地与P糖蛋白的疏水性口袋契合, 二者结合程度高.     

Quantitative Series Enrichment Analysis (QSEA): a novel procedure for 3D-QSAR analysis

A novel procedure is proposed for 3D-QSAR analysis. The composition of 16 published QSAR datasets has been examined using Quantitative Series Enrichment Analysis (QSEA). The procedure is based on topomer technologies. A heatmap display in combination with topomer CoMFA and a novel series trajectory analysis revealed critical information for the assembly of structures into meaningful series. Global and local centroid structures can be determined from a similarity distance matrix and build the origins for stepwise model building by increasing the similarity radius around the centroid nucleus. The results indicate that the new procedure allows determination of whether compounds belong to an emerging structure-activity relationship and which compounds can be predicted within reliable limits.     

CoMFA,CoMSIA, Topomer CoMFA,HQSAR, molecular docking and molecular dynamics simulations study of triazine morpholino derivatives as mTOR inhibitors for the treatment of breast cancer

mTOR has become a promising target for many types of cancer like breast, lung and renal cell carcinoma. CoMFA, CoMSIA, Topomer CoMFA and HQSAR were performed on the series of 39 triazine morpholino derivatives. CoMFA analysis showed q² value of 0.735, r²_cv value of 0.722 and r²_pred value of 0.769. CoMSIA analysis (SEHD) showed q² value of 0.761, r²_cv value of 0.775 and r²_pred value of 0.651. Topomer CoMFA analysis showed q² value of 0.693, r² (conventional correlation coefficient) value of 0.940 and r²_pred value of 0.720. HQSAR analysis showed q²,r²and r²_pred values of 0.694, 0.920 and 0.750, respectively. HQSAR analysis with the combination of atomic number (A), bond type (B) and atomic connections showed q² and r² values of 0.655 and 0.891, respectively. Contour maps from all studies provided significant insights. Molecular docking studies with molecular dynamics simulations were carried out on the highly potent compound 36. Furthermore, four acridine derivatives were designed and docking results of these designed compounds showed the same interactions as that of the standard PI-103 which proved the efficiency of 3D-QSAR and MD/MS study. In future, this study might be useful prior to synthesis for the designing of novel mTOR inhibitors.     

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.     

Molecular modeling studies of human immunodeficiency virus type 1 protease inhibitors using three‐dimensional quantitative structure‐activity relationship,virtual screening,and docking simulations

In this paper, two 3‐dimensional quantitative structure‐activity relationship models for 60 human immunodeficiency virus (HIV)‐1 protease inhibitors were established using random sampling analysis on molecular surface and translocation comparative molecular field vector analysis (Topomer CoMFA). The non–cross‐validation (r²), cross‐validation (q²), correlation coefficient of external validation (Q²_ext), and F of 2 models were 0.94, 0.80, 0.79, and 198.84 and 0.94, 0.72, 0.75, and 208.53, respectively. The results indicated that 2 models were reasonable and had good prediction ability. Topomer Search was used to search R groups in the ZINC database, 20 new compounds were designed, and the Topomer CoMFA model was used to predicate the biological activity. The results showed that 18 new compounds were more active than the template molecule. So the Topomer Search is effective in screening and can guide the design of new HIV/AIDS drugs. The mechanism of action was studied by molecular docking, and it showed that the protease inhibitors and Ile50, Asp25, and Arg8 sites of HIV‐1 protease have interactions. These results have provided an insight for the design of new potent inhibitors of HIV‐1 protease.     

A selectivity study of benzenesulfonamide derivatives on human carbonic anhydrase II/IX by 3D-QSAR,Molecular Docking and Molecular Dynamics Simulation

Nowadays, different approaches have been pursued with the intent to develop sulfonamide-like carbonic anhydrase inhibitors that possess better selectivity profiles toward the different human isoforms of the enzyme. Here, we used conventional 3D-QSAR methods, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and Topomer CoMFA, to construct three-dimensional quantitative structure-activity relationship (3D-QSAR) models for benzenesulfonamide derivatives as human carbonic anhydrase (hCA) II/IX inhibitors. The theoretical models had good reliability (R²>0.75) and predictability (Q²>0.55), and the contour maps could graphically present the contributions of the force fields for activity and identify the structural divergence between human carbonic anhydrase II inhibitors and human carbonic anhydrase IX inhibitors. Consequently, we explored the selectivity of inhibitor for human carbonic anhydrase II and IX through molecular docking, and the difference of activity coincides with the potential binding mode well. According to the results of the predicted values and the molecule docking, we found that the inhibitors published in the literature had stronger inhibition on the hCA IX; based on the theoretical models, we designed seven new compounds with good potential activity and reasonably good ADMET profile, which could selectively inhibit hCA IX. Molecular Dynamics Simulation showed that newly-designed compound D7 had good selectivity on hCA IX. The findings from 3D-QSAR and docking studies maybe helpful in the rational drug design of isoform-selective inhibitors.     

Insight into the structural requirements of pyrimidine-based phosphodiesterase 10A (PDE10A) inhibitors by multiple validated 3D QSAR approaches

Schizophrenia is a complex disorder of thinking and behaviour (0.3?0.7% of the population is affected). The over-expression of phosphodiesterase 10A (PDE10A) enzyme may be a potential target for schizophrenia and Huntington’s disease. Because 3D QSAR analysis is one of the most frequently used modelling techniques, in the present study, five different 3D QSAR tools, namely CoMFA, CoMSIA, kNN-MFA, Open3DQSAR and topomer CoMFA methods, were used on a dataset of pyrimidine-based PDE10A inhibitors. All developed models were validated internally and externally. The non-commercial Open3DQSAR produced the best statistical results amongst 3D QSAR tools. The structural interpretations obtained from different methods were thoroughly analysed and were justified on the basis of information obtained from the crystal structure. Information from one method was mostly validated by the results of other methods and vice versa. In the current work, the use of multiple tools in the same analysis revealed more complete information about the structural requirements of these compounds. On the basis of the observations of the 3D QSAR studies, 12 new compounds were designed for better PDE10A inhibitory activity. The current investigation may help in further designing new PDE10A inhibitors with promising activity.