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。     

基于分子对接的苯丙素甙（PPGs）类化合物的虚拟筛选和合理设计

采用虚拟化合物生成法对抗肿瘤的苯丙素甙(PPGs)类化合物进行了配体受体对 接研究。以三种不同的骨架结构为基础分别生成了五十个虚拟苯丙素甙(PPGs)类化 合物，并将它们与端粒DNA受体进行分子对接，分析已知结构的对接结果，通过虚 拟筛选的方法得到了一批与受体相互作用能较高并且复合物能量较低的新的有潜力 的活性化合物。该方法可以弥补分子对接研究中，只能计算药物与受体的相互作用 ，无法有效设计新化合物的不足。这种方法在基于结构的药物分子设计中具有重要 的意义。     

比较分子场分析研究哒嗪酮的体系的三维构效关系

用比较分子场分析（CoMFA）法对两个体系的化合物进行了研究，得到了预报能力较强的模型. 初步的研究表明，作为一种三维定量构效（3D-QSAR）方法，它能够揭示分子三维结构对活性的贡献，有较广阔的应用前景．     

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。     

R基团搜索技术用于HIV-1逆转录酶抑制剂的分子设计

本文采用基于R基团搜索技术的Topomer CoMFA方法对41个人类免疫缺陷病毒(HIV-1)逆转录酶抑制剂进行了三维定量构效关系(3D-QSAR)分析。所得优化模型的拟合、交互验证及外部验证的复相关系数分别为0.995、0.859和0.945。采用Topomer Search技术对ZINC数据库进行R基团的虚拟筛选,得到R贡献高的基团,以活性最高的13号分子为模板进行过滤得到1个Ra基团和20个Rb基团。并以此设计得到20个新化合物分子,其中有19个化合物的预测活性值高于13号分子。研究结果表明,所建立的Topomer CoMFA模型具有良好的稳定性和预测能力,基于R基团的Topomer Search技术可以有效筛选并设计出新的HIV-1逆转录酶抑制剂,为抗艾滋病新药设计提供了理论依据。     

嘧啶（氧）苯甲酸类除草剂的3D—QSAR研究

利用比较分子场分析 (Co MFA)方法 ,对 2 0种嘧啶 (氧 )苯甲酸类化合物进行了三维定量构效关系(3 D-QSAR)研究。得到了具有较强预测能力的 QSAR模型。并对此模型进行了验证 ,在此基础上 ,设计了具有更高活性的化合物。     

6-氮杂吲唑类Pim-1激酶抑制剂的3D-QSAR、药效团模型研究和分子设计

Pim-1 激酶通过作用于多种信号通路或靶点影响肿瘤的发生发展,近年来被认为是肿瘤治疗的良好靶标。本文采用SYBYL-X2. 1. 1软件中的TopomerCoMFA、GALAHAD模块建立计算机模型,研究39个基于6-氮杂吲唑环的Pim-1激酶抑制剂的三维定量构效关系及药效团特征元素。结果显示,TopomerCoMFA建模所得交叉验证系数（q2）和相关系数（r2）分别为0. 756和0. 951,结合外部验证表明此3D-QSAR模型具有较高预测能力及较好的统计学稳定性,同时,用等势图描述了R1、R2基团处立体场、静电场对活性的具体影响。药效团研究结果表明,含氢键受体的芳香杂环母核结构,以及侧链取代基中含有芳香杂环结构对化合物的活性贡献较大。最后根据上述模型信息新设计了15个Pim-1激酶抑制剂分子并完成活性预测及分子对接模式研究,其中4个分子的预测pIC50高于建模分子中活性最好的化合物17,Surflex-Dock分析显示新设计分子均与Pim-1激酶形成较强氢键相互作用。基于6-氮杂吲唑环的Pim-1激酶抑制剂的3D-QSAR模型以及药效团模型可用于指导新型抑制剂的结构优化,为设计和开发具有较高活性的新型Pim-1激酶抑制剂提供有效帮助。     

2(1H)-喹啉-2,4-二酮类化合物抗小麦锈病的3D-QSAR研究

用比较分子力场分析(CoMFA)方法和比较分子相似性指数分析(CoMSIA)方法研究了21个2(1H)-喹啉-2,4-二酮类化合物抗小麦锈病的三维定量构效关系(3D-QSAR),发现用CoMFA方法可以找到最佳的3D-QSAR模型,并通过量子化学从头计算的方法研究了不同活性化合物的前线轨道及静电势分布图的差异.所得构效关系模型为发现更高活性的化合物提供理论指导.     

基于他克林的乙酰胆碱酯酶抑制剂的3D-QSAR研究及虚拟筛选

本文通过对58个他克林派生物乙酰胆碱酯酶抑制剂分子进行建模分析,研究其结构与活性的关系,并通过虚拟筛选方法获得一系列潜在AChE抑制剂双位点分子。首先将一系列他克林二联体化合物与AChE晶体结构对接,获得化合物的活性构象,以此进行建模分析,建立结构与活性之间的三维定量构效关系。所得模型CoMFA、CoMSIA、TopomerCoMFA的交叉验证系数分别为0.510、0.702、0.571,非交叉验证系数为0.998、0.988、0.794,测试集r_(pred)~2为0.750、0.742、0.766,所得模型具有良好的预测性,由此可以为设计高活性的新分子提供理论基础。然后,使用Topomer search对ZINC数据库中的125909分子进行虚拟筛选,得到891个具有潜在AChE抑制活性的分子。最后,对这891个分子进行分子对接,观察分子与晶体结构的结合情况,筛选得到66个具有高选择性的双位点AChE抑制剂分子。     

基于计算机模拟技术对JAK2抑制剂的定量构效关系、分子设计和分子动力学研究

本文选取了52个对Janus激酶2(JAK2)有抑制作用的小分子化合物,分别使用3D-QSAR中的CoMFA和CoMSIA方法构建了两个可靠的、具有预测能力的模型,并利用分子对接分析数据集化合物与JAK2蛋白的相互作用,表明化合物主要通过氢键和范德华作用与JAK2靶蛋白结合。根据3D-QSAR模型的分析结果,设计了40个化合物,利用构建的模型预测其抑制活性;使用软件预测了化合物的药代动力学(ADME)参数,开展分子对接模拟,最终选择化合物D01和D22与JAK2靶蛋白进行了分子动力学模拟研究,结果显示两个复合物结合构象稳定,与分子对接结果趋势一致。本研究的结果可以为JAK2抑制剂的研发提供一些新的思路,为临床开发此类药物提供理论支撑。     

虚拟活性化合物的自动生成

虚拟活性化合物的自动生成是从药效团和预先设定的结构碎片出发,通过碎片选择、碎片组装和柔性构象搜索来获得虚拟活性结构。经过对HIV-1蛋白酶抑制剂药效团进行虚拟活性化合物生成,得到了16个虚拟活性化合物,通过构象分析发现生成的化合物满足药效团的限制条件。说明这一方法能够有效的生成虚拟活性结构,与药效团检索结果对比发现生成的虚拟活性结构新颖易于合成。     

Virtual Screening and Structure Generation Applied to Drug Design

The methods of computer-aided drug design can be divided into two categories according to whether or not the structures of receptors are known1, corresponding to two principal strategies: (1) searching the bio-active ligands against virtual combinatorial libraries and calculating the affinity energy between ligand and receptor by docking ; (2) QSAR and 3D-structure data-mining. 3D-QSAR method is now applied widely to drug discovery, but this method is generally limited to refine the structu…     

Structure-based rational quest for potential novel inhibitors of human HMG-CoA reductase by combining CoMFA 3D QSAR modeling and virtual screening

3-Hydroxy-3-methylglutaryl-coenzyme A reductase (HMGR) catalyzes the formation of mevalonate. In many classes of organisms, this is the committed step leading to the synthesis of essential compounds, such as cholesterol. However, a high level of cholesterol is an important risk factor for coronary heart disease, for which an effective clinical treatment is to block HMGR using inhibitors like statins. Recently the structures of catalytic portion of human HMGR complexed with six different statins have been determined by a delicate crystallography study (Istvan and Deisenhofer Science 2001, 292, 1160-1164), which established a solid basis of structure and mechanism for the rational design, optimization, and development of even better HMGR inhibitors. In this study, three-dimensional quantitative structure-activity relationship (3D QSAR) with comparative molecular field analysis (CoMFA) was performed on a training set of up to 35 statins and statin-like compounds. Predictive models were established by using two different ways: (1) Models-fit, obtained by SYBYL conventional fit-atom molecular alignment rule, has cross-validated coefficients (q2) up to 0.652 and regression coefficients (r2) up to 0.977. (2) Models-dock, obtained by FlexE by docking compounds into the HMGR active site, has cross-validated coefficients (q2) up to 0.731 and regression coefficients (r2) up to 0.947. These models were further validated by an external testing set of 12 statins and statin-like compounds. Integrated with CoMFA 3D QSAR predictive models, molecular surface property (electrostatic and steric) mapping and structure-based (both ligand and receptor) virtual screening have been employed to explore potential novel hits for the HMGR inhibitors. A representative set of eight new compounds of non-statin-like structures but with high pIC(50) values were sorted out in the present study.     

On the topological sub-structural molecular design (TOSS-MODE) in QSPR/QSAR and drug design research

A recently introduced graph-theoretical approach to the study of structure-property-activity relationships is presented. The theoretical approach and the computational strategy for the use of the TOSS-MODE approach are given with details. Several QSPR and QSAR applications are reviewed including the study of physical properties of organic compounds, diamagnetic susceptibilities, and biological properties. The applications of the TOSS-MODE approach to discrimination of active/inactive compounds, the virtual screening of compounds with a desired property from databases of chemical structures, identification of active/inactive fragments and its relationships with 2D/3D pharmacophores, and to the design of novel compounds with desired biological activities are also reviewed.     

On the Topological Sub-Structural Molecular Design (TOSS-MODE) in QSPR/QSAR and Drug Design Research

Abstract

A recently introduced graph-theoretical approach to the study of structure-property-activity relationships is presented. The theoretical approach and the computational strategy for the use of the TOSS-MODE approach are given with details. Several QSPR and QSAR applications are reviewed including the study of physical properties of organic compounds, diamagnetic susceptibilities, and biological properties. The applications of the TOSS-MODE approach to discrimination of active/inactive compounds, the virtual screening of compounds with a desired property from databases of chemical structures, identification of active/inactive fragments and its relationships with 2D/3D pharmacophores, and to the design of novel compounds with desired biological activities are also reviewed.     

A new protocol for predicting novel GSK-3β ATP competitive inhibitors

Glycogen synthase kinase 3β (GSK-3β) is a potential therapeutic target for cancer, type-2 diabetes, and Alzheimer's disease. This paper proposes a new lead identification protocol that predicts new GSK-3β ATP competitive inhibitors with topologically diverse scaffolds. First, three-dimensional quantitative structure-activity relationship (3D QSAR) models were built and validated. These models are based upon known GSK-3β inhibitors, benzofuran-3-yl-(indol-3-yl) maleimides, by means of comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Second, 28?826 maleimide derivatives were selected from the PubChem database. After filtration via Lipinski's rules, 10?429 maleimide derivatives were left. Third, the FlexX-dock program was employed to virtually screen the 10?429 compounds against GSK-3β. This resulted in 617 virtual hits. Fourth, the 3D QSAR models predicted that from the 617 virtual hits, 93 compounds would have GSK-3β inhibition values of less than 15 nM. Finally, from the 93 predicted active hits, 23 compounds were confirmed as GSK-3β inhibitors from literatures; their GSK-3β inhibition ranged from 1.3 to 480 nM. Therefore, the hits rate of our virtual screening protocol is greater than 25%. The protocol combines ligand- and structure-based approaches and therefore validates both approaches and is capable of identifying new hits with topologically diverse scaffolds.     

二氢吡啶类化合物的三维定量构效关系

通过分子力学和量子化学计算,得出两种二氢吡啶衍生物的低能构象,再应用比较分子力场分析方法(CoMFA)和比较分子相似性指数分析方法(CoMSIA)分别对两种构象的43个二氢吡啶衍生物进行3D-QSAR研究. 计算结果表明,用两种方法建立的两种构象的构效关系模型均有较好的预测能力.通过分析CoMFA和CoMSIA的系数等势图,直观地了解二氢吡啶衍生物的结构对生物活性的影响,为进一步设计高活性的二氢吡啶衍生物提供一定的理论依据.     

光系统Ⅱ(PSⅡ)抑制剂的比较分子场研究

以光系统Ⅱ抑制剂DISCO(DIStanceCOmparisons)模型的活性构象分子作为模板,利用比较分子场分析方法对三类结构不同的化合物进行了三维构效关系的研究。研究结果有助于对DISCO重叠模型的评估的新型PSⅡ抑制剂的设计与合成。     

Integration of genetic virtual screening patterns and latent multivariate modeling techniques for QSAR optimization based on combinations and/or interactions between peptides and proteins

Both the concept and the model of snug quantitative structure-activity relationship (QSAR) were pro-posed and developed for molecular design through constructing QSAR based on some known mode of receptor/ligand interactions. Many disadvantages of traditional models can be avoided by using the proposed method because the traditional models only determined upon molecular structural features in sample sets themselves. A genetic virtual screening of peptide/protein combinations (GVSPPC) is proposed for the first time by utilizing this idea to examine peptide/protein affinity activities. A genetic algorithm (GA) was developed for screening combinative targets with an interaction mode for virtual receptors. GVSPPC succeeds in disposing difficulties in rational QSAR,in order to search for the ligand/receptor interactions on conditions of unknown structures. Some bioactive oligo-/poly-peptide systems covering 58 angiotensin converting enzyme (ACE) inhibitors and 18 double site mutation residues in camel antibody protein cAb-Lys3 were investigated by GVSPPC with satisfactory results (R 2 cu>0.91,Q 2 cv > 0.86,ERMS=0.19-0.95),respectively,which demonstrates that GVSPPC is more inter-pretable in the ligand-receptor interaction than the traditional QSAR method.