酪氨酸激酶抑制剂的三维定量构效关系研究

利用柔性原子受体模型(FLARM)方法对一系列的异黄酮和喹诺酮衍生物表皮生长因子受体酪氨酸激酶抑制剂进行了三维定量构效关系研究，得到了合理的构效关系模型.FLARM方法的计算结果还给出了虚拟的受体模型，该模型说明了抑制剂与受体之间可能的相互作用.由该虚拟受体模型得到的受体-配体相互作用与Novartis药效团模型比较类似.     

IOPY/ISPY类HIV-1逆转录酶抑制剂的定量构效关系研究

C-5修饰的3-碘-4-芳氧基/芳硫基吡啶酮(IOPY/ISPY)类化合物是一类潜在的HIV-1非核苷类逆转录酶抑制剂, 特别是这类化合物因具有同时抑制野生型和突变型病毒株的特性, 而受到更加广泛的关注. 首先利用两套2D通用描述符同时构建了该类化合物的线性和非线性定量构效关系模型. 结果表明这些模型都具有较好的预测能力, 并且非线性模型较线性模型预测能力更好些. 为了更好、更形象地描述逆转录酶抑制剂的特征, 进一步结合三维定量构效关系(3D-QSAR)模型, 以及SAReport分析对该类化合物同时抑制野生型和突变型病毒株的结构特征进行了分析, 发现在对这类化合物进行结构修饰时, 需要服从如下三条理论指导原则: (1) R基团中的正负电场分布情况对化合物的活性起着关键作用|(2) R基团最好具有芳香环或芳香杂环和(3) R基团的环结构上连接的取代基不宜太多.     

酪氨酸激酶抑制剂的比较分子场分析

采用比较分子场分析(CoMFA)方法研究了一组嘧啶类衍生物酪氨酸激酶抑制剂活性与结构的关系.所得模型不仅能够很好地预报训练集中的化合物的活性,而且还可以准确地预报预报集中的化合物活性.通过分析分子场等值面图在空间的分布,可以观察到叠加分子周围的立体和静电特征对化合物活性的影响.     

Discodermolide及其衍生物三维定量构效关系的研究

Discodermolide是一种新颖的作用于微管蛋白的抗肿瘤化合物, 具有良好的药用前景. 为了设计出药效更好的类似物, 我们用比较分子力场分析(CoMFA)和比较分子相似性指数分析(CoMSIA)对discodermolide及其衍生物进行了三维定量构效关系(3D-QSAR)的研究, 并建立了相关的预测模型. 其中, CoMFA模型的交叉验证相关系数(q2)为0.592, 非交叉验证相关系数(r2)为0.982, 标准偏差(SEE)为0.094, F值为119.761; CoMSIA模型的q2为0.544, r2为0.980, SEE为0.098, F值为108.715. 计算结果表明, 获得的CoMFA和CoMSIA模型具有良好的预测能力, 可以应用于指导该类化合物的设计.     

紫杉醇衍生物的三维定量构效关系研究

利用比较分子力场分析(CoMFA)方法对98个紫杉醇衍生物的三维定量构效关系(3D-QSAR)进行了分析,发现影响其活性的立体能与静电能之比为61.6/38.4.进一步分析表明,C-13侧链基团的改变对其活性影响较大,尤其是2'-OH对保持活性是至关重要的;而母环其它位置取代基的改变对活性影响较小.该模型交叉验证r_cv²=0.714,非交叉验证r₂=0.901;以此模型对验证组10个化合物活性进行预测,r_pred²=0.812,表明模型具有很好的预测能力,对紫杉醇的改性或新类似物的合成具有指导意义.     

HEPT类逆转录酶抑制剂的三维定量构效关系

利用比较分子力场分析(CoMFA)方法对32个HEPT类HIV-1逆转录酶抑制剂(RTIs)的三维定量构效关系(3D-QSAR)进行了分析,建立了HIV-1逆转录酶抑制剂的3种3D-QSAR模型,发现影响其生物活性的主要因素为立体场因素,这与HIV-1RT的非底物结合部位(NNBS)的疏水性环境相吻合.进一步分析表明,适当长度的1-位侧链对保持化合物的抗病毒活性致关重要;增大5-位取代基的体积可增强生物活性;在1-位苄氧甲基的对位引入大体积基团有利于提高活性.同时考察立体场、静电场与生物活性的关系,表明,CoMFA模型为最佳预测模型,其交叉验证系数R_CV²=0.870,传统相关系数R²=0.986,标准偏差SE=0.146,F=294.546.用此模型预测了检验组3个HEPT类化合物的-lgEC₅₀,R_pred²=0.850,表明模型具有很好的预测能力,可为HEPT类HIV-1逆转录酶抑制剂的结构优化提供理论指导.     

酪氨酸蛋白磷酸酯酶1B抑制剂的分子对接和三维定量构效关系研究

用一种柔性分子对接方法(FlexX)将12个2-草酰胺苯甲酸类抑制剂和酪氨酸蛋白磷酸酯酶(PTP1B)活性口袋进行分子对接,对接程序预测的抑制剂和酶之间的相互作用能与抑制活性之间有很好的相关性(非线性相关系数R²达0.859),这说明对接结果可以比较准确地预测抑制剂和PTP1B之间的结合模式.然后,将33个同类抑制剂的骨架叠合在分子对接预测的结合构象上,用比较分子力场分析方法(CoMFA)对其进行三维定量活性构效关系研究,得到的CoMFA模型具有很好的统计相关性(交互验证回归系数q²为0.650),并可以准确地预测测试集6个化合物的活性(平均标准偏差为0.177).同时,由CoMFA模型得出的抑制剂改造信息与用FlexX预测的结合模式是一致的,进一步证明我们预测的结合模式是正确的.为研究这类抑制剂和PTP1B的结合模式及对抑制剂进行结构改造提供了信息.     

吲哚衍生物类VEGFR-2酪氨酸激酶抑制剂的从头设计

以血管内皮生长因子受体-2(VEGFR-2)酪氨酸激酶的晶体结构为基础, 采用从头药物设计方法, 设计了一系列吲哚类化合物, 并用类药性和分子对接进行了筛选, 最后得到10个对接能量较低的化合物分子, 对具有最低结合能的化合物与VEGFR-2酪氨酸激酶的复合物进行了10 ns的分子动力学模拟, 并对其结合模式进行了分析. 这些化合物结构新颖, 可能作为抗肿瘤的先导化合物或候选药物. 本文结果为VEGFR-2酪氨酸激酶抑制剂的进一步改造、 设计及合成提供了理论基础, 并有助于开发高活性和高选择性的抗肿瘤药物.     

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.     

芳香噻嗪类衍生物作为选择性醛糖还原酶抑制剂的分子对接和基于受体的三维定量构效关系研究

芳香噻嗪类衍生物被证明是一类选择性较好的高活性醛糖还原酶抑制剂(ARIs).本文对44个芳香噻嗪类化合物进行了分子对接(docking)和三维定量构效关系(3D-QSAR)研究,并探索了此类化合物与醛糖还原酶(ALr²)的作用机理.醛糖还原酶与醛还原酶(ALR1)活性位点的叠加结果显示, ALr²中残基Leu 300和Cys298的存在是化合物1m具有高选择性的原因.分别建立了比较分子场分析方法(CoMFA, q² = 0.649, r² =0.934; q²:交叉验证相关系数, r²:非交叉验证相关系数)和比较分子相似性指数分析方法(CoMSIA, q² = 0.746, r² = 0.971)模型,并对影响此类化合物生物活性的结构进行了鉴定.结果显示,两个模型均具有较高预测能力,并通过测试集中的7个化合物进行了验证,其中CoMFA模型和CoMSIA模型的预测相关系数(r_Pred²)分别为0.748和0.828. 3D-QSAR模型中的三维等值线图表明,在化合物1m的苄基环上C3和C4位置以及苯并噻嗪母核上C5和C7位置进行改进可能对生物活性的提高有利,此预测与我们前期报道的苯并噻嗪母核C7位改进结果一致.本文所建3D-QSAR模型能够在理性设计具有更高生物活性的新型ARIs中发挥重要作用.     

小分子蛋白酪氨酸激酶抑制剂的合成研究进展

蛋白酪氨酸激酶在肿瘤的形成和增殖中起着关键作用,以蛋白酪氨酸激酶作为肿瘤治疗靶点的研究受到极大关注.本文作者综述了近十年来不同结构的酪氨酸激酶抑制剂的合成以及抗肿瘤活性的研究进展,以期为酪氨酸激酶抑制剂的研究与开发提供参考.     

The structure-activity relationship of inhibitors of serotonin uptake and receptor binding

Summary An analysis of five different datasets of inhibitors of serotonin uptake has yielded quantitative structure/ activity relationships (QSARs) which delineate the role of steric and hydrophobic properties essential for inhibition by phenylethylamine-type analogues.     

Synthesis,crystal structure and activity evaluation of novel 3,4‐dihydro‐1‐benzoxepin‐5(2H)‐one derivatives as protein–tyrosine kinase (PTK) inhibitors

Four new 3,4‐dihydro‐1‐benzoxepin‐5(2H )‐one derivatives, namely (E )‐4‐(5‐bromo‐2‐hydroxybenzylidene)‐6,8‐dimethoxy‐3,4‐dihydrobenzo[b ]oxepin‐5(2H )‐one, ( 7 ), (E )‐4‐[(E )‐3‐(5‐bromo‐2‐hydroxyphenyl)allylidene]‐6,8‐dimethoxy‐3,4‐dihydrobenzo[b ]oxepin‐5(2H )‐one, ( 8 ), (E )‐4‐(5‐bromo‐2‐hydroxybenzylidene)‐6‐hydroxy‐8‐methoxy‐3,4‐dihydrobenzo[b ]oxepin‐5(2H )‐one, C₁₈H₁₅BrO₅, ( 9 ), and (E )‐4‐[(E )‐3‐(5‐bromo‐2‐hydroxyphenyl)allylidene]‐6‐hydroxy‐8‐methoxy‐3,4‐dihydrobenzo[b ]oxepin‐5(2H )‐one, ( 10 ), have been synthesized and characterized by FT–IR, NMR and MS. The structure of ( 9 ) was confirmed by single‐crystal X‐ray diffraction. Crystal structure analysis shows that molecules of ( 9 ) are connected into a one‐dimensional chain in the [010] direction through classical hydrogen bonds and these chains are further extended into a three‐dimensional network via C—H…O interactions. The inhibitory activities of these compounds against protein–tyrosine kinases (PTKs) show that 6‐hydroxy‐substituted compounds ( 9 ) and ( 10 ) are more effective for inhibiting ErbB1 and ErbB2 than are 6‐methoxy‐substituted compounds ( 7 ) and ( 8 ). This may be because ( 9 ) and ( 10 ) could effectively bind to the active pockets of the protein through intermolecular interactions.     

An improved ensemble learning machine for biological activity prediction of tyrosine kinase inhibitors

Boosting is one of the most important strategies in ensemble learning because of its ability to improve the stability and performance of weak learners. It is nonparametric, multivariate, fast and interpretable but is not robust against outliers. To enhance its prediction accuracy as well as immunize it against outliers, a modified version of a boosting algorithm (AdaBoost R2) was developed and called AdaBoost R3. In the sampling step, extremum samples were added to the boosting set. In the robustness step, a modified Huber loss function was applied to overcome the outlier problem. In the output step, a deterministic threshold was used to guarantee that bad predictions do not participate in the final output. The performance of the modified algorithm was investigated with two anticancer data sets of tyrosine kinase inhibitors, and the mechanism of inhibition was studied using the relative weighted variable importance procedure. Investigating the effect of base learner's strength reveals that boosting is only successful using the classification and regression tree method (a weak to moderate learner) and does not have a significant effect using the radial basis functions partial least square method (a strong base learners). Copyright © 2015 John Wiley & Sons, Ltd.     

Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design

In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus.     

Whether proton transition to the triphosphate tail of ATP occurs at protein kinase environment: A Car‐Parrinello ab initio molecular dynamics study

Protonation state of the triphosphate tail of ATP (adenosine triphosphate) in protein environment is a fundamental issue, which has significant impact on the mechanism investigation of biochemical processes with ATP involved. Proton transition from surroundings (water molecule coordinating to magnesium, H_W; amino group of Lys, H_L) to the ATP tail in the catalytic core of protein kinase found recently disproved the commonly accepted deprotonation state of ATP tail. In this account, Car‐Parrinello ab initio molecular dynamics (CP‐AIMD) method has been employed to examine whether the proton transition occurs. To provide a comparison basis for the dynamics simulations, static quantum mechanics (QM), and combined quantum mechanics and molecular mechanics (QM/MM) calculations have also been carried out. Consistent results have been obtained that complete transition of hydrogen from the surroundings to the triphosphate tail of ATP is not allowed. The most dominant conformations correspond to the ones with H_W bonding to O(W) and H‐bonding to O(ATP), [O(W)‐H_W···O(ATP)], H_L bonding to N(Lys) and H‐bonding to O(ATP), [N(Lys)‐H_L···O(ATP)]. Metastable structures with one hydrogen atom bonding with two heavy atoms (hydrogen acceptors) were also located by our dynamic simulations. This bonding mode can satisfy the hungering for hydrogen of the two heavy atoms simultaneously. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2008     

Approach on quantitative structure-activity relationship for design of a pH neutral carrier containing tertiary amino group

The quantitative structure-activity relationship (QSAR) for neutral carriers used to prepare hydrogen ion sensors has been studied. A series of synthesized carrier compounds were taken as the training set. Five molecular structure parameters of the compounds were calculated by using CNDO/2 algorithm and used as feature variables in constructing QSAR model. The lower and upper limits of the linear pH response range were taken as the activity measure. The corresponding model equations were derived from the stepwise regression procedure. With the established QSAR model, a new pH carrier, (4-hydroxybenzyl) didodecylamine (XIII) was proposed and synthesized. The PVC membrane pH electrode based on carrier XIII with a wide pH linear response range of 2.0-12.5 was prepared. Having a theoretical Nernstian response slope of 57.2 ± 0.3 mV/pH (n = 5 at 25 °C) without a super-Nernstian phenomenon, the sensor had low resistance, short response time, high selectivity and good reproducibility. Moreover, the sensor was successfully applied to detecting the pH value of serum samples.     

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.