新型酪氨酸激酶小分子抑制的三维药效团研究

通过CATALYST软件包得到了两类HER2抑制的三维药效团模型。尽管亚苄基丙二腈化合物和3-取代吲哚啉-2-酮系列化合物具有完全不同的骨架结构,但得到的药效团却具有共同的特性,这表明当这两类抑制剂和受体发生相互作用时,采用了相似的结合模式。共同的药效团模型包括一个氢键受体,一个氢键给体,一个脂肪类疏水团以及一个芳香类疏水团。根据药效团模型,我们还进行了三维构效关系的研究,结果表明得到的药效团模型具有很好的预测能力(线性回归系数R≈0.96)。药效团模型对于研究酪氨酸激酶小分子抑制剂的结构与活性关系,以及评估和预测此类未知化合物活性具人重要的意义。     

新型酪氨酸激酶小分子抑制的三维药效团研究

通过CATALYST软件包得到了两类HER2抑制的三维药效团模型。尽管亚苄基丙二腈化合物和3-取代吲哚啉-2-酮系列化合物具有完全不同的骨架结构,但得到的药效团却具有共同的特性,这表明当这两类抑制剂和受体发生相互作用时,采用了相似的结合模式。共同的药效团模型包括一个氢键受体,一个氢键给体,一个脂肪类疏水团以及一个芳香类疏水团。根据药效团模型,我们还进行了三维构效关系的研究,结果表明得到的药效团模型具有很好的预测能力(线性回归系数R≈0.96)。药效团模型对于研究酪氨酸激酶小分子抑制剂的结构与活性关系,以及评估和预测此类未知化合物活性具人重要的意义。     

糖原合成酶激酶抑制剂模型的建立

选取四类共89种活性较高的糖原合成酶激酶（GSK-3）抑制剂作为分子训练集，利用CATALYST系统，经构象分析，分子叠合等过程构建出药效团模型。筛选出具有一个氢键受体，一个芳香疏水中心，两个环芳香性的药效团模型 (weight=2.4,RMS=0.50, null cost- fixed cost =104, correlation coefficient=0.95)。该模型具有较强的预测活性能力，可用于优化分子结构，找到高效低毒的化合物。     

基于药效团模型从中草药数据库中搜索gpIIb/IIIa受体抑制剂

选择20 个3,4-二氢-1(1H)-异喹啉酮类gpIIb/IIIa受体抑制剂作为训练集, 利用Catalyst软件包建立了gpIIb/IIIa受体抑制剂三维药效团模型. 探讨了药效团作用模式. 并通过建立的可靠性最佳的药效团模型(线性回归系数r=0.7715), 从中草药数据库中虚拟筛选了gpIIb/IIIa受体抑制剂, 通过实验活性测定得到了8个抑制ADP活化全血血小板聚集的IC50从40到100 μmol·L-1的化合物, 进一步证明了所建药效团模型的有效性.     

酪氨酸激酶抑制剂的柔性原子受体模型方法研究

用柔性原子受体模型方法对一系列嘧啶类衍生物酪氨酸激酶抑制剂进行了3D－ QSAR研究，建立了相关性很好的模型，这些模型对测试集中化合物活性的预测结果 表明其具有较强的预测能力。柔性原子受体模型方法还给出了虚拟的受体模型，表 明了受体和配体之间可能的相互作用，包括两个氢键相互作用、一个疏水作用和一 个硫－芳香相互作用，这与Novartis的药效团模型非常一致。     

基于药效团模型从中草药数据库中搜索gpⅡb/Ⅲa受体抑制剂

选择20个3,4-二氢-1(1H)-异喹啉酮类gpⅡb/Ⅲa受体抑制剂作为训练集,利用Catalyst软件包建立了gpⅡb/Ⅲa受体抑制剂三维药效团模型.探讨了药效团作用模式.并通过建立的可靠性最佳的药效团模型(线性回归系数r=0.7715),从中草药数据库中虚拟筛选了gpⅡb/Ⅲa受体抑制剂,通过实验活性测定得到了8个抑制ADP活化全血血小板聚集的IC50从40到100μmol·L-1的化合物,进一步证明了所建药效团模型的有效性.     

HMG-CoA还原酶抑制剂三维药效团的构建

以作用于鼠肝脏细胞的21个3-羟基-3-甲基戊二酰辅酶A(HMG-CoA)还原酶抑制剂(RI)为训练集, 训练集化合物具备结构多样性, 来源于相同药理模型, 活性值IC50范围在0.3-8000 nmol·L-1. 利用Catalyst 计算HMG-CoA还原酶抑制剂最优药效团由一个氢键受体, 一个氢键给体, 一个疏水基团和一个芳香环特征组成. 药效团模型Fixed cost值, Total cost值和Configuration cost值分别为88.75、111.5 和16.98. 训练集化合物活性计算值与实测值相关系数为0.8883, 偏差值为1.269, 交叉验证结果表明, 药效团模型具有较高的置信度, 对测试集化合物活性值的预测结果显示有较好的预测能力, 可用于数据库搜索发现新的具有该活性的化合物, 也可用于中药或天然产物药物的研究开发.     

黄酮类醛糖还原酶抑制剂的抑制机理研究

为了研究黄酮类醛糖还原酶抑制剂的抑制机理, 选择了31个黄酮类化合物作为训练集, 使用Catalyst软件包构建了此类抑制剂的药效团模型. 并专门针对黄酮类化合物定制了氢键给体和受体模型, 效果优于使用Catalyst内预定义的模型. 最终的药效团模型由两个氢键给体和一个氢键受体组成, 对训练集具有较好预测能力(Correl=0.9013). 此外, 使用InsightII/Affinity对6个黄酮类化合物进行了分子对接研究. 综合药效团模型和分子对接研究的结果, 发现黄酮类化合物的抑制活性主要源于黄酮骨架上的C4’或C3’位的羟基与醛糖还原酶活性口袋中的TYR48、VAL47、GLN49和C7位的羟基与HIS110, TRP111所形成的两组氢键.     

5-HT3受体拮抗剂药效团模型的构建

以31个来源于MDDR数据库中具有抑制鼠Bezold-Jarisch反射作用的5-HT3受体拮抗剂作为训练集化合物, 构建5-HT3受体拮抗剂药效团模型. 训练集化合物具备结构多样性, 来源于相同药理模型, 活性值ED50范围为0.05~320 μg/kg i.v.. 利用Catalyst计算5-HT3受体拮抗剂的最优药效团由一个氢键受体、一个疏水基团、一个正电离子化基团、一个芳香环特征和6个排除体积组成; Fixed cost值、Null cost 值、Δcost值和Configuration cost值分别为112.6, 172.0, 59.4和7.248. 训练集化合物活性的计算值与实测值相关系数为0.9031, 偏差值为0.8976, 基于Fischer的交叉验证结果表明药效团模型具有较高的置信度, 所得药效团对训练集化合物活性值的预测结果显示有较好的预测能力, 可用于数据库搜索指导发现新的具有该活性的先导化合物, 也可用于中药或天然产物药物研究开发.     

基于药效团的BRD4靶向小分子抑制剂虚拟筛选

BRD4靶点和多种肿瘤密切相关,是具有良好成药性的热门靶点。本文选取活性较好且结构差异较大的BRD4小分子抑制剂作为训练集分子,基于配体小分子共同特征(HipHop)方法使用Discovery Studio 3.0分子模拟软件构建了药效团。药效团通过测试集验证、ROC曲线验证(SE(sensitivity)=0.93765、SP(specificity)=0.89500、(AUC)=0.956),结果表明构建得到的药效团具有较强的可靠性和较高的可信度。药效团模型含有1个芳环中心、1个疏水基团、2个氢键受体四个药效特征元素。此药效团被用于ZINC数据库进行虚拟筛选,共筛选了861203个分子,命中率为0.782%。再对筛选得到的分子经过分子对接、ADMET成药性预测、构象分析并讨论分子-蛋白相互作用模式,最终得到了21个有潜力的BRD4小分子抑制剂。     

Identification of novel PfDHODH inhibitors as antimalarial agents via pharmacophore-based virtual screening followed by molecular docking and in vivo antimalarial activity

Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) catalyses the fourth reaction of de novo pyrimidine biosynthesis in parasites, and represents an important target for the treatment of malaria. In this study, we describe pharmacophore-based virtual screening combined with docking study and biological evaluation as a rational strategy for identification of novel hits as antimalarial agents. Pharmacophore models were established from known PfDHODH inhibitors using the GALAHAD module with IC₅₀ values ranging from 0.033 μM to 142 μM. The best pharmacophore model consisted of three hydrogen bond acceptor, one hydrogen bond donor and one hydrophobic features. The pharmacophore models were validated through receiver operating characteristic and Günere–Henry scoring methods. The best pharmacophore model as a 3D search query was searched against the IBS database. Several compounds with different structures (scaffolds) were retrieved as hit molecules. Among these compounds, those with a Q_FIT value of more than 81 were docked in the PfDHODH enzyme to further explore the binding modes of these compounds. In silico pharmacokinetic and toxicities were predicted for the best docked molecules. Finally, the identified hits were evaluated in vivo for their antimalarial activity in a parasite inhibition assay. The hits reported here showed good potential to become novel antimalarial agents.     

Pharmacophore Mode lingand Virtual Screening to Design the Potential Influenza Virus Endonuclease Inhibitors

Influenza virus endonuclease is an attractive target for antiviral therapy in the treatment of influenza infection. The purpos e of this study is to design a novel antiviral agent with improved biological activities against the influenza virus endonuclease. In this study, chemical feature‐based 3D pharmacophore models were developed from 41 known influenza virus endonuclease inhibitors. The best quantitative pharmacohore model (Hypo 1), which consists of two hydrogen‐bond acceptors and two hydrophobic features, yields the highest correlation coefficient (R = 0.886). Hypo 1 was further validated by the cross validation method and the test set compounds. The application of this model for predicting the activities of 11 known influenza virus endonuclease inhibitors in the test set shows great success. The correlation coefficient of 0.942 and a cross validation of 95;% confidence level prove that this model is reliable in identifying structurally diverse compounds for influenza virus endonuclease inhibition. The most active compound (compound 1) from the training set was docked into the active site of the influenza virus endonuclease as an additional verification that the pharmacophore model is accurate. The docked conformation showed important hydrogen bond interactions between the compound and two amino acids, Lys 134 and Lys 137. After validation, this model was used to screen the NCI chemical database to identify new influenza virus endonuclease inhibitors. Our study shows that the to pranking compound out of the 10 newly identified compounds using fit value ranking has an estimated activity of 0.049 μM. These newly identified lead compounds can be further experimentally validated using in vitro techniques.     

Ligand-based molecular modeling study on a chemically diverse series of cholecystokinin-B/gastrin receptor antagonists: generation of predictive model

Pharmacophore hypotheses were developed for six structurally diverse series of cholecystokinin-B/gastrin receptor (CCK-BR) antagonists. A training set consisting of 33 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 2100 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond donors, one hydrophobic aliphatic, and one hydrophobic aromatic feature, had a correlation (r) of 0.884 and a root-mean-square deviation of 1.1526, and the cost difference between null cost and fixed cost was 81.5 bits. The model was validated on a test set consisting of six different series of 27 structurally diverse compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug-like molecules to retrieve new chemical entities as potent CCK-BR antagonists. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.     

3D-QSAR and receptor modeling of tyrosine kinase inhibitors with flexible atom receptor model (FLARM)

A set of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors was investigated with the aim of developing 3D-QSAR models using the Flexible Atom Receptor Model (FLARM) method. Some 3D-QSAR models were built with high correlation coefficients, and the FLARM method predicted the biological activities of compounds in test set well. The FLARM method also gave the pseudoreceptor model, which indicates the possible interactions between the receptor and the ligand. The possible interactions include two hydrogen bonds, one hydrophobic interaction, and one sulfur-aromatic interaction, which are in accord with those in the pharmacophore model given by the scientists at Novartis. This shows that the FLARM method can bridge 3D-QSAR and receptor modeling in computer-aided drug design. Pharmacophore can be obtained according to these results, and 3D searching can then be done with databases to find the lead compound of EGFR tyrosine kinase inhibitors.     

β分泌酶抑制剂的药效团模型研究

选择活性跨越0.002至25 μmol•L－1的4类共25个β分泌酶抑制剂作为训练集, 使用Catalyst软件包构建出药效团模型, 并通过对药效团的有效性分析, 筛选得到的最佳模型(correlCorrel＝0.969, Config＝16.32, Δcost＝62.422)由一个环芳香性、一个疏水中心、一个正电荷中心和一个氢键供体组成. 并用其它209个抑制剂组成测试集对模型进行验证, 结果表明该模型显示出较强的预测能力, 能够为进一步的数据库搜索, 寻找新型的β分泌酶抑制剂先导物提供依据.     

人类免疫缺陷病毒整合酶二酮酸类抑制剂的三维药效团构建

应用遗传算法相似性程序(GASP), 以作用于I型人类免疫缺陷病毒(human immun-odeficiency virus type 1, HIV-1)整合酶(IN)的二酮酸类(diketoacids, DKAs)抑制剂构建药效团模型. 所选训练集分子均具有可靠的类药性特征及DKAs药效团特征. 尝试将抑制剂与药效团叠合后的构象和抑制剂与IN的对接构象进行叠合, 得到药效团模型与分子对接构象中IN残基的相对位置, 并基于抑制剂的药效团模型特征与周围IN氨基酸残基位置的匹配情况进行药效团特征的修改. 所得最优药效团由1个疏水特征、3对氢键特征和1个氢键供体特征组成. 该药效团的命中物质量(goodness of hit, GH)为0.56, 产出率(Y)达63.6%, 假阳性率(FP)为0.41%. 该药效团具有较好的置信度, 产出率较高而假阳性率较低, 可用于数据库搜索发现新的具有DKAs药效团特征的活性化合物, 也可为先导化合物的改造提供帮助.     

3D-pharmacophore models for selective A2A and A2B adenosine receptor antagonists

Three-dimensional pharmacophore models were generated for A2A and A2B adenosine receptors (ARs) based on highly selective A2A and A2B antagonists using the Catalyst program. The best pharmacophore model for selective A2A antagonists (Hypo-A2A) was obtained through a careful validation process. Four features contained in Hypo-A2A (one ring aromatic feature (R), one positively ionizable feature (P), one hydrogen bond acceptor lipid feature (L), and one hydrophobic feature (H)) seem to be essential for antagonists in terms of binding activity and A2A AR selectivity. The best pharmacophore model for selective A2B antagonists (Hypo-A2B) was elaborated by modifying the Catalyst common features (HipHop) hypotheses generated from the selective A2B antagonists training set. Hypo-A2B also consists of four features: one ring aromatic feature (R), one hydrophobic aliphatic feature (Z), and two hydrogen bond acceptor lipid features (L). All features play an important role in A2B AR binding affinity and are essential for A2B selectivity. Both A2A and A2B pharmacophore models have been validated toward a wide set of test molecules containing structurally diverse selective antagonists of all AR subtypes. They are capable of identifying correspondingly high potent antagonists and differentiating antagonists between subtypes. The results of our study will act as a valuable tool for retrieving structurally diverse compounds with desired biological activities and designing novel selective adenosine receptor ligands.