含氟农药的比较分子场分析研究

用比较分子场分析(CoMFA)方法对112种含氟农药分子的生物活性及毒性同时进行了定量构效关系研究。用78个化合物作为训练集,以距离比较方法(DISCO)确认的药效团为叠合规则构建CoMFA模型,发现影响活性的立体场与静电场的贡献分别为60.4%和39.6%,影响毒性的立体场与静电场的贡献分别为59.2%和40.8%。药效模型与毒效模型在交叉验证时的相关系数平方(R^2)分别为0.652和0.611,非交叉验证的R^2分别为0.982和0.977,方差比F(8,69)值分别为463.6及362.9,活性和毒性的标准偏差-极差比s/△γ值分别为3.6%和2.9%,表明模型具有较好的自预测能力。对测试组34个化合物进行了活性和毒性的预测,活性与毒性预测的标准偏差-极差比s/△γ值分别为10.4%和6.4%。最后,还建立了一个由97个化合物构建的扩大的模型,各种统计量得到了进一步提高。并预计了一个活性较高且毒性很低的新化合物。     

基于QSAR模型的有机磷化合物毒性预测

随着有机磷化合物(OPs)的广泛应用,其在越来越多的环境介质中被检测出来。大多数OPs具有毒性,但人们缺乏快速且有效的预测手段来对毒性进行评估。本文将结合E-Dragon软件计算的分子描述符,采用不同的QSAR模型对36个OPs的毒性进行预测。文中采用后退法作为描述符筛选方法,以均方根误差(RMSE)作为评价标准,共找到14个对线性核函数支持向量机(SVM)模型贡献较大的描述符;在最终得到的SVM模型交叉验证结果中,计算值与实际值的相关系数为0. 913,均方根误差为0. 388;外部测试验证结果中,平均相对误差为9. 10%。此外,采用多元线性回归(MLR)、人工神经网络(ANN)以及偏最小二乘回归(PLS)模型对OPs的毒性进行预测,交叉验证结果显示,三个模型的计算值与实际值的相关系数分别为0. 878、0. 686与0. 620,没有SVM模型的预测能力好。因此采用线性核函数的SVM模型对OPs进行毒性预测是一个行之有效的方法。     

马来酰亚胺类GSK-3β抑制剂的QSAR研究及分子设计

为了研究马来酰亚胺类化合物抑制活性与分子结构的定量构效关系,在分子理论的基础上计算了67个马来酰亚胺类化合物的电性距离矢量,通过最佳变量子集回归的方法,建立了抑制活性的五元线性回归模型,模型的传统相关系数(R2)和交叉验证相关系数(RCV2)分别为0.864和0.825.该模型经过Jackknife法检验、交叉验证、F检验及外部检验法证明具有良好的稳健性和预测能力.根据进入模型的5个变量分析,影响马来酰亚胺类GSK-3β抑制剂抑制活性的主要结构基团是-NH-,=O(或-OH),≡CH,Cl-及-O-(或-S-).同时基于QSAR模型设计了6个抑制活性显著提高的马来酰亚胺类分子,并预测了它们的抑制活性.     

q~2引导构象选择CoMFA方法研究HIV－1RT抑制剂

对未知受体结构的药物设计其主导方法CoMFA来说，柔性目标分子的多种构象 造成了问题的复杂性。本文介绍交叉验证参数R~2(q~2)引导的构象选择CoMFA方法 ，选择化合物的最佳构象。将一组47个HIV－1 RT抑制剂进行有、无构象选择的 CoMFA分析来作评价。根据化合物的活性、毒性、选择性指数（毒性／活性比）等 实验数据得到的模型，其交叉验证参数q~2为0.7以上，非交叉验证的相应参数为0. 94以上，最后，还经过试验集化合物验证该模型的预测能力，置信度（1－α）＞ 0.99。     

神经网络在ACE抑制肽QSAR中的应用研

通过对天然氨基酸的457种物化性质参数进行主成分分析后得到SVHEHS描述符,用该描述符分别对血管紧张素转化酶(ACE)抑制二肽、三肽、四肽进行表征,并建立了肽结构与活性的神经网络模型。ACE抑制二肽神经网络模型的相关系数、交叉验证相关系数、均方根误差和外部验证相关系数分别为0.946、0.951、0.249、0.852,三肽模型分别为0.973、0.945、0.135、0.813,四肽模型分别为0.915、0.879、0.250、0.814。由此表明SVHEHS描述符结合神经网络对ACE抑制肽的建模效果及模型预测能力均较理想,在此基础上进一步通过平均影响值(Mean impact value,MIV)法确定了显著影响各类肽活性的结构因素,从而为新的强活性ACE抑制肽的分子设计提供了理论基础。     

磺胺基羟肟酸类HDAC抑制剂三维定量构效关系

组蛋白去乙酰化酶(HDAC)对染色质分布和基因调节起着重要的作用,也是治疗癌症和其它疾病的新靶点.羟肟酸类抑制剂是目前研究最多的组蛋白去乙酰化酶抑制剂.应用比较分子力场(CoMFA)法对一系列磺胺基羟肟酸类HDAC抑制剂进行了结构活性关系研究,得到的模型具有较高的交叉验证系数(q²=0.704).并在此基础上,建立了非交叉验证的偏最小二乘分析(PLS)模型.用该模型对随机选择的6个化合物组成的测试集进行了预测,得到了令人满意的结果,所建模型具有良好的预测能力.本研究对于设计高活性的HDAC抑制剂及抗癌药物都有指导意义.     

顺式新烟碱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34个顺式新烟碱类衍生物的杀虫活性进行三维定量构效关系(3D-QSAR)研究.构建的CoMFA和CoMSIA模型的交叉验证系数rc2v分别为0.877和0.862,非交叉验证系数r2分别为0.970和0.961,表明建立的3D-QSAR模型具有较好的统计相关性和预测能力.一系列的研究结果指出:立体场、静电场和氢键受体场是描述顺式新烟碱类衍生物的化学结构与杀虫活性关系的重要参数;在咪唑啉环的3,4位不宜引入较大的取代基,提高咪唑啉环的电负性或增强硝基一个端氧的氢键受体特征有利于提高顺式新烟碱类衍生物的杀虫活性.     

取代嘧啶衍生物抗前列腺癌活性的3D-QSAR研究

运用比较分子力场分析(CoMFA)方法,建立18种取代嘧啶衍生物抗前列腺癌活性(pM)的三维定量构效关系。训练集中15个化合物用于建立预测模型,测试集13个化合物(含10号模板分子和新设计的9个分子)作为模型验证。建立的CoMFA模型的交叉验证系数(R_ev²)、非交叉验证系数(R²)分别为0.344、0.935,说明所建模型具有较强的鲁棒性和良好的预测能力。该模型中立体场、静电场贡献率依次为71.6%、28.6%。影响取代嘧啶衍生物抗前列腺癌活性的主要因素是取代基的疏水作用和空间位阻,其次是取代基的库仑力、氢键及配位作用。基于此研究结果,设计了9个新化合物,其抗前列腺癌活性有待医学实验验证。     

新型三唑并噻二唑衍生物对PTP1B的抑制活性的比较分子力场分析研究

基于比较分子力场分析(CoMFA)方法建立21种新型三唑并噻二唑衍生物对PTP1B的抑制活性(pMP)的三维定量构效关系(3D-QSAR)。训练集中17个化合物用于建立预测模型,测试集5个化合物作为模型验证。已建立的CoMFA模型的交叉验证系数(Rcv2)、非交叉验证系数(R2)分别为0.432、0.975,说明所建模型具有较强的稳定性和良好的预测能力。该模型中立体场、静电场贡献率依次为59.2%、40.8%,表明影响抑制活性(pMP)的主要因素是取代基的空间位阻及疏水性,其次是取代基的氢键及配位作用。基于此研究结果,设计了3个具有较高抑制活性的新化合物,有待医学实验验证。     

5,8-二甲基乙酰紫草素衍生物QSAR及分子设计

综合运用密度泛函理论、分子力学、统计学及比较分子力场分析等方法研究了抗肿瘤药物5,8-二甲基乙酰紫草素衍生物的二维(2D)、三维(3D)定量构效关系(QSAR).所建最优2D-QSAR方程的交叉验证系数(q2)和拟合相关系数(R2)分别为0.618和0.736.CoMFA模型的q2和非交叉验证系数(r2)分别为0.703和0.982,预测相关系数R2pred为0.746.结果表明,所建立的2D/3D-QSAR模型都具有良好的统计学意义及合理、可信的预报能力,可以预测未知化合物的活性。基于此研究结果,设计了4个具有较高抗肿瘤活性的新化合物.     

Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain. Snooker does not require knowledge of ligands, is therefore suitable for apo-proteins, and can be applied to all receptors of the GPCR protein family. The method comprises the construction of a homology model of the TM domains and prioritization of residues on the probability of being ligand binding. Subsequently, protein properties are converted to ligand space, and pharmacophore features are generated at positions where protein ligand interactions are likely. Using this semiautomated knowledge-driven bioinformatics approach we have created pharmacophore hypotheses for 15 different GPCRs from several different subfamilies. For the beta-2-adrenergic receptor we show that ligand poses predicted by Snooker pharmacophore hypotheses reproduce literature supported binding modes for ～75% of compounds fulfilling pharmacophore constraints. All 15 pharmacophore hypotheses represent interactions with essential residues for ligand binding as observed in mutagenesis experiments and compound selections based on these hypotheses are shown to be target specific. For 8 out of 15 targets enrichment factors above 10-fold are observed in the top 0.5% ranked compounds in a virtual screen. Additionally, prospectively predicted ligand binding poses in the human dopamine D3 receptor based on Snooker pharmacophores were ranked among the best models in the community wide GPCR dock 2010.     

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer’s Diseases via Pharmacophore Modeling,Auto-QSAR,and Molecular Docking Approaches

Neurodegenerative diseases, for example Alzheimer’s, are perceived as driven by hereditary, cellular, and multifaceted biochemical actions. Numerous plant products, for example flavonoids, are documented in studies for having the ability to pass the blood-brain barrier and moderate the development of such illnesses. Computer-aided drug design (CADD) has achieved importance in the drug discovery world; innovative developments in the aspects of structure identification and characterization, bio-computational science, and molecular biology have added to the preparation of new medications towards these ailments. In this study we evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies, in comparison with standard drugs. The results indicated that the pharmacophore models produced from structures of AChE, BChE and MAO could identify the active compounds, with a recuperation rate of the actives found near 100% in the complete ranked decoy database. Moreso, the robustness of the virtual screening method was accessed by well-established methods including enrichment factor (EF), receiver operating characteristic curve (ROC), Boltzmann-enhanced discrimination of receiver operating characteristic (BEDROC), and area under accumulation curve (AUAC). Most notably, the compounds’ pIC₅₀ values were predicted by a machine learning-based model generated by the AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best models achieved for AChE, BChE and MAO were models kpls_radial_17 (R² = 0.86 and Q² = 0.73), pls_38 (R² = 0.77 and Q² = 0.72), kpls_desc_44 (R² = 0.81 and Q² = 0.81) and these externally validated models were utilized to predict the bioactivities of the lead compounds. The binding affinity results of the ligands against the three selected targets revealed that luteolin displayed the highest affinity score of −9.60 kcal/mol, closely followed by apigenin and ellagic acid with docking scores of −9.60 and −9.53 kcal/mol, respectively. The least binding affinity was attained by gallic acid (−6.30 kcal/mol). The docking scores of our standards were −10.40 and −7.93 kcal/mol for donepezil and galanthamine, respectively. The toxicity prediction revealed that none of the flavonoids presented toxicity and they all had good absorption parameters for the analyzed targets. Hence, these compounds can be considered as likely leads for drug improvement against the same.     

Pharmacophore based 3D-QSAR modeling,virtual screening and docking for identification of potential inhibitors of β-secretase

The enzyme β-secretase-1 is responsible for the cleavage of the amyloid precursor protein, a vital step in the process of the formation of amyloid-β peptides which are known to lead to neurodegeneration causing Alzheimer’s disease. Challenges associated with toxicity and blood brain permeation inability of potential inhibitors, continue to evade a successful therapy, thus demanding the search and development of highly active and effective inhibitors. Towards these efforts, we used a ligand based pharmacophore model generation from a dataset of known inhibitors whose activities against β-secretase hovered in the nano molar range. The identified 5 feature pharmacophore model, AHHPR, was validated via three dimensional quantitative structure activity relationship as indicated by r², q² and Pearson R values of 0.9013, 0.7726 and 0.9041 respectively. For a dataset of compounds with nano molar activity, the important pharmacophore features present in the current model appear to be similar with those observed in the models resulting from much wider activity range of inhibitors. Virtual screening of the ChemBridge CNS-Set™, a database having compounds with a better suitability for central nervous system based disorders followed by docking and analysis of the ligand protein interactions resulted in the identification of eight prospective compounds with considerable diversity. The current pharmacophore model can thus be useful for the identification, design and development of potent β-secretase inhibitors which by optimization can be potential therapeutics for Alzheimer’s disease.     

基于药效团模型的DHODH抑制剂构效关系研究

利用药效团模型研究二氢乳清酸脱氢酶(Dihydroorotate dehydrogenase,DHODH)抑制剂的构效关系,为DHODH抑制剂的虚拟筛选提供新的方法.以31个具有DHODH抑制活性的化合物为训练集化合物,半数抑制浓度(IC50)范围为7~63000 nmol/L,利用Catalyst/HypoGen算法构建DHODH抑制剂药效团模型,通过对训练集化合物多个构象进行叠合,提取药效团特征及三维空间限制构建药效团模型.利用基于CatScramble的交叉验证方法及评价模型对已知活性化合物的活性预测能力,确定较优药效团模型.模型包含1个氢键受体、3个疏水中心,表征了受体配体相互作用时可能发生的氢键相互作用、疏水相互作用和π-π相互作用,4个药效特征在三维空间的排列概括了DHODH抑制剂产生活性的结构特点.所得较优模型对训练集化合物及测试集化合物的计算活性值与实验活性值的相关系数分别为0.8405和0.8788.利用药效团模型对来源于微生物的系列化合物进行虚拟筛选,筛选出59个预测活性较好的化合物,可作为进一步药物研发的候选化合物.     

药效团检索设计新的HIV-1蛋白酶抑制剂

通过对自建的未开发化合物三维结构库进行药效团检索,得到了4个对HIV-1蛋白酶抑制活化的化合物,通过构象分析发现包含药效团的构象处于优势构象,而且4个结构都含有带两个邻位羟基的苯环和一个间位羰基的药效团以及公共子结构。通过计算发现它们的疏水参数都很小。在考虑满足包含药效团的结构特征和有适中的疏水参数两个因素的前提下,设计出了新的具有潜在抑制HIV-1蛋白酶活性的化合物。它们的结构都比检索得到的四个化合物更为简单,因此易于合成。     

Study of Aldo-keto Reductase 1C3 Inhibitor with Novel Framework for Treating Leukaemia Based on Virtual Screening and In vitro Biological Activity Testing

Aldo-keto reductase 1C3(AKR1C3) is a potential target for the treatment of acute myeloid leukaemia and T-cell acute lymphoblastic leukaemia. In this study, pharmacophore models, molecular docking and virtual screening of target prediction were used to find a potential AKR1C3 inhibitor. Firstly, eight bacteriocin derivatives(Z1-Z8) were selected as training sets to construct 20 pharmacophore models. The best pharmacophore model MODEL_016 was obtained by Decoy test(the enrichment degree was 21.5117, and the fitting optimisation degree was 0.9668). Secondly, MODEL_016 was used for the virtual screening of ZINC database. Thirdly, the hit 83256 molecules were docked into the AKR1C3 protein. Compared to the total scores and interactions between compounds and protein, 16532 candidate compounds with higher docking scores and interactions with important residues PHE306 and TRP227 were screened. Lastly, eight compounds(A1-A8) that had good absorption, distribution, metabolism, excretion and toxicity(ADMET) properties were obtained by target prediction. Compounds A3 and A7 with high total score and good target prediction results were selected for in vitro biological activity test, whose IC₅₀ values were 268.3 and 88.94 μmol/L, respectively. The results provide an important foundation for the discovery of novel AKR1C3 inhibitors. The research methods used in this study can also provide important references for the research and development of new drugs.     

3D QSAR analyses of novel tyrosine kinase inhibitors based on pharmacophore alignment

In an effort to develop a quantitative ligand-binding model for the receptor tyrosine kinases, a pharmacophore search was first used to identify structural features that are common in two novel sets of 12 molecules of the 3-substituted indolin-2-ones and 19 compounds of the benzylidene malononitriles with low-to-high affinity for HER2, a kind of receptor tyrosine kinase. The common pharmacophore model based on these 31 compounds was used as a template to obtain the aligned molecular aggregate, which provided a good starting point for 3D-QSAR analysis of only the 19 benzylidene malononitriles. Two molecular field analysis (MFA) techniques, including CoMFA and CoMSIA, were used to derive the quantitative structure-activity relationships of the studied molecules. From the studied results, it was obvious that the 3D-QSAR models based on the pharmacophore alignment were superior to those based on the simple atom-by-atom fits. Considering the flexibility of the studied molecules and the difference between the active conformers and the energy-lowest conformers, the pharmacophore model can usually provide the common features for the flexible regions. Moreover, the best CoMSIA model based on the pharmacophore hypothesis gave good statistical measure from partial least-squares analysis (PLS) (q(2) = 0.71), which was slightly better than the CoMFA one. Our study demonstrated that pharmacophore modeling and CoMSIA research could be effectively combined. Results obtained from both methods helped with understanding the specific activity of some compounds and designing new specific HER2 inhibitors.     

人类腺苷受体A3亚型拮抗剂的构效关系分析

构建人类腺苷受体A3亚型药效团模型和三维蛋白结构模型用于作用模式研究.以18个来源于文献具有腺苷受体A3亚型拮抗活性的化合物作为训练集,使用HypoGen方法构建药效团模型.通过同源模建和分子动力学模拟构建了人类腺苷受体A3亚型的三维蛋白模型,并利用PROCHECK方法评估该模型的合理性,对所得的结构使用分子对接程序进行作用模式分析,药效团模型和同源模建结果相互匹配较好.使用新药效团模型对MDL药物数据库(MDDR)中包含的约120000个化合物进行虚拟筛选,得到了8个候选化合物,用于进一步的生物学评价和活性测定.本工作对于人类腺苷受体A3亚型拮抗剂的设计和抗哮喘药物的研发具有一定的理论指导和应用价值.     

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。