吡啶并嘧啶类Wee1激酶衍生物的三维定量构效关系与分子对接

采用比较分子场分析方法(CoMFA)和比较分子相似性指数分析方法(CoMSIA)对一系列吡啶并嘧啶类衍生物进行了三维定量构效关系(3D-QSAR)研究,建立了CoMFA和CoMSIA两种模型. 所构建的最佳模型的交叉验证相关系数分别为0.707和0.645,非交叉验证系数分别是0.964和0.972,模型的一些外部验证表明两个模型合理、可靠,并具有良好的预报能力. 同时,用分子对接的方法分析了该类化合物与Wee1激酶结构的作用模式,结果进一步表明,在R1和R5取代基上引入正电性基团,R2为体积小的电负性基团,同时选择体积中等和强的推电子的R3但亲水性的X取代基,能有效改善这类化合物的抑制活性.     

抗癌性苯并噻唑类衍生物三维定量构效关系研究

应用比较分子场分析法(CoMFA)对一系列抗癌性苯并噻唑类衍生物进行了三维定量构效关系(3D-QSAR)研究，建立了交叉验证的CoMFA模型，并在此基础上建立了非交叉验证的偏最小二乘分析模型．所建最佳模型的交叉验证相关系数为0.642，非交叉验证相关系数为0.976，估算的标准误差S＝0.161,统计方差比F(3;20)＝111.4，表明该模型是合理有效的．同时对该系列抗癌化合物的3D-QSAR进行了深入研究．结果表明，在R取代基的第一个原子上引入吸电子基团或原子，如F等，便可增强与它直接相连的C19的正电荷，从而增强C19ˉ位上处于静电场蓝色区域的原子的正电荷．同时，选择适当体积大小的取代基R，使之落入立体场绿色区域，就能有效地改善这类化合物的抗癌活性．     

几种咪唑-二氧杂环的含能取代衍生物的理论研究

使用DFT的B3LYP方法对几种咪唑二氧杂环化合物的分子结构、红外光谱、生成焓、爆轰性能和化学/热稳定性进行了研究.四种不同含能基团-NO₂,-NH₂,-N₃和-ONO₂对该化合物各项性能的影响进行了比较.结果表明-NO₂和-ONO₂基团有效地增加了化合物的密度,而-N³基团极大地增加了化合物的生成焓.其中-NO₂取代物爆轰性能接近1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane,-ONO₂取代物的爆轰性能低于hexahydro-1,3,5-trinitro-1,3,5-triazine.自然键轨道分析表明,这些化合物中相对较弱的键为取代基和咪唑环之间的键,以及二氧杂环中的C-O键.吸电子基团(-NO₂, -N³和-ONO₂)在分子中产生了明显的诱导效应,减弱了基团与咪唑环之间的链接,降低了对应键的键裂解能.研究表明基团的电负性与化合物的稳定性有关.考虑到化合物的爆轰性能和热稳定性,DNTNDI化合物满足高能量密度材料的要求.     

用相互作用势指数估算单取代烷烃生成焓

基于直链烷烃生成焓的实验值提出16种取代基X(OH、SH、NH₂、Br、Cl、I、NO₂、CN、CHO、COOH、CH₃、CH=CH₂、C≡CH、Ph、COCH₃、COOCH₃)的相互作用势指数IPI(X). 用IPI(X)和极化效应指数建立模型,对单取代烷烃RX(包括含支链的化合物)的生成焓进行估算,所得回归方程有良好的相关性,该模型既考虑了基团R和X的贡献,又考虑了R与X相互作用的贡献. 并采用留一法对其稳定性和预测能力进行验证.     

基于4，8-二氢二呋咱[3，}4-b，e]吡嗪高能材料的设计与筛选

为了寻找高能量密度的材料,本文设计了一系列基于4,8-二氢二呋咱[3,4-b,e]吡嗪的含能材料. 利用密度泛函理论研究了它们结构与性质之间的关系. 结果表明,这些设计化合物的性质受到含能基团和杂环取代基的影响. -N₃含能基团是提高设计化合物生成热的最有效取代官能团,而四唑环/-C(NO₂)₃基团对炸药的爆轰性能有较大贡献. 键解离能分析表明,引入-NHNH₂,-NHNO₂,-CH(NO₂)₃和-C(NO₂)₃基团会显著降低键解离能. 由于化合物A8,B8,C8,D8,E8和F8具有良好的爆轰性能和热稳定性,最终被筛选为潜在的高能密度材料. 此外,还计算了这些筛选化合物的电子结构.     

亚锗基卡宾及取代亚锗基卡宾与环氧乙烷抽提反应机理的密度泛函理论研究

采用B3LYP/6-311G(d,p)方法研究了单重态亚锗基卡宾及取代亚锗基卡宾X₂Ge=C:(X=H, F, Cl, CH₃)与环氧乙烷的氧转移反应机理. 结果表明, 由于环氧乙烷中氧上的2p孤对电子向X₂Ge=C:中C上的2p空轨道迁移,形成了p→p授受键,从而生成了各中间体. 随着p→p授受键的不断加强(即C-O键的逐渐缩短),中间体经过渡态生成了抽提产物. 取代基的电负性是影响该类反应的主要因素,取代基的电负性越大,反应的活化能越小     

程序性死亡蛋白配体1抑制剂2-甲基-3-二苯基甲醇衍生物的定量结构活性关系研究

本文研究了二十种2-甲基-3-二苯基甲醇衍生物衍生物作为PD-L1抑制剂的定量结构活性关系. 用密度泛函理论在B3LPY/6-31+G(d,p)水平对它们的结构和性质进行计算，求得最高占有轨道能级E_HOMO、最低空轨道能级E_LUMO、总能量E_T、 偶极矩DM、绝对硬度?、绝对电负性χ、软度S、电负性ω和能隙ΔE等性质. 用主成分分析(PCA)、多元线性回归(MLR)和多元非线性回归(MNLR)建立它们的定量结构活性关系(QSAR). 基于统计分析构建定量模型并预测化合物的性质. MLR和MNLR统计结果的相关系数R²分别为0.661和0.758. 用留一法交叉检验(LOO-CV)，r²_m检验和“Golbraikh&Tropsha”标准分析用于检验MLR和MNLR模型的稳健性. 结果表明两种模型均具有统计显著性和稳健性，两种模型均能预测其生物活性，可用于PD-L1免疫检查点抑制剂生物活性的预估.     

基于Topomer CoMFA方法对芳基硫代吲哚衍生物的分子建模与设计

采用Topomer CoMFA方法对30个芳基硫代吲哚衍生物进行三维定量关系研究,建立了3D-QSAR模型,所得模型的交叉验证相关系数q~2,非交叉验证相关系数r~2,外部验证的复相关系数Q_(ext)~2分别为0.562,0.878,0.985,结果表明该模型具有较好的稳定性和预测能力.Topomer CoMFA模型等势面提供的立体场与静电场可视化图像,直观的揭示了这一系列化合物中不同取代基结构对其生物活性的影响,运用这些信息进行分子设计,在理论上获得了5个具有较高活性的新化合物,该QSAR的实验结果可为合成新药提供理论参考.     

噻吩并嘧啶衍生物抗胃癌活性的CoMFA模型与分子设计

基于比较分子力场分析(CoMFA)方法建立25种噻吩并嘧啶衍生物抗胃癌活性(pM)的三维定量构效关系(3D-QSAR)。训练集中20个化合物用于建立预测模型,测试集6个化合物(含模板分子)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0.369、0.831,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40.9%、59.1%,表明影响抑藻活性(pI)的主要因素是取代基的库仑力、氢键及配位,其次是取代基的疏水性和空间位阻。基于此研究结果,设计了4个具有较高抗胃癌活性的新化合物,有待医学实验验证。     

无定形靶中离子注入的R,Rp,△Rp的理论计算

本文在Thomas-Fermi势能基础上,导出了全射程R的解析解:R=2/a[E^1/2-A₁(arctg(2E^1/2-f)/△^1/2+arctg f/△^1/2)+B₁ln((E^1/2-f)²)/(E-fE^1/2+d) ·d/f²],其中A₁,B₁,f,d和△均为与离子及靶的质量、原子序数有关的常数。结合导出的η=R/(R_p)(R_p指投影射程)比值的双曲线函数关系 η=F(μ)[A₂(μ)+(B₂(μ))/(ε^1/2+C)],和ω=R_p/△R_p(△R_p指投影射程的标准偏差)比值的线性关系ω=A₃(μ)ε^1/21/2+B₃(μ),可简便而又准确地计算R,△R_p,R_p.这里F(μ),A₂(μ),B₂(μ), B₃(μ)和A₃(μ)为μ的代数函数,μ为离子与靶的质量比,C是经验常数.并对η等关系式的物理意义作了讨论。上述公式的计算结果与Gibbons的数值解结果及有关实验结果作了比较,表明可用于元素半导体如Si、二元化合物如GaAs以及三元化合物如SiO₂等;既对较轻离子适用,也对重离子适用,具有一定的普适范围。     

Integration-mediated prediction enrichment of quantitative model for Hsp90 inhibitors as anti-cancer agents: 3D-QSAR study

The present study describes a systematic 3D-QSAR study consisting of pharmacophore modeling, docking, and integration of ligand-based and structure-based drug design approaches, applied on a dataset of 72 Hsp90 inhibitors as anti-cancer agents. The best pharmacophore model, with one H-bond donor (HBD), one H-bond acceptor (HBA), one hydrophobic_aromatic (Hy_Ar), and two hydrophobic_aliphatic (Hy_Al) features, was developed using the Catalyst/HypoGen algorithm on a training set of 35 compounds. The model was further validated using test set, external set, Fisher’s randomization method, and ability of the pharmacophoric features to complement the active site amino acids. Docking analysis was performed using Hsp90 chaperone (PDB-Id: 1uyf) along with water molecules reported to be crucial for binding and catalysis (Sgobba et al. ChemMedChem 4:1399–1409, 2009). Furthermore, an integration of the ligand-based as well as structure-based drug design approaches was done leading to the integrated model, which was found to be superior over the best pharmacophore model in terms of its predictive ability on internal [integrated model 2: R _(train) = 0.954, R _(test) = 0.888; Hypo-01: R _(train) = 0.912 and R _(test) = 0.819] as well as on external data set [integrated model 2: R _(ext.set) = 0.801; Hypo-01: R _(ext.set) = 0.604].     

A combined 3D-QSAR and molecular docking strategy to understand the binding mechanism of V600EB-RAF inhibitors

B-RAF is a member of the RAF protein kinase family involved in the regulation of cell growth, differentiation, and proliferation. It forms a part of conserved apoptosis signals through the RAS?CRAF?CMAPK pathway. ^V600EB-RAF protein has much potential for scientific research as therapeutic target due to its involvement in human melanoma cancer. In this work, a molecular modeling study was carried out for the first time with 3D-QSAR studies by following the docking protocol on three different data sets of ^V600EB-RAF inhibitors. Based on the co-crystallized compound (PDB ID: 1UWJ), a receptor-guided alignment method was utilized to derive reliable CoMFA and CoMSIA models. The selected CoMFA model gives the best statistical values (q ² =?0.753, r ² =?0.962). With the same alignment protocol, a statistically reliable CoMSIA model out of fourteen different combinations was also derived (q ² = 0.807, r ² = 0.961). The actual predictive powers of both models were rigorously validated with an external test set, which gave satisfactory predictive r ² values for CoMFA and CoMSIA models, 0.89 and 0.88, respectively. In addition, y-randomization test was also performed to validate our 3D-QSAR models. Contour maps from CoMFA and CoMSIA models supported statistical results, revealed important structural features responsible for biological activity within the active site and explained the correlation between biological activity and receptor?Cligand interactions. Based on the developed models few new structures were designed. The newly predicted structure (IIIa) showed higher inhibitory potency (pIC₅₀ 6.826) than that of the most active compound of the series.     

基于Topomer CoMFA的苯磺酰基亚胺噻唑衍生物的三维定量构效关系研究和分子设计

采用Topomer CoMFA方法对21个苯磺酰基亚胺噻唑衍生物进行三维定量构效关系研究,得到了HIV-1非核苷类逆转录酶抑制剂的3D-QSAR模型,其拟合复相关系数r2=0.964,交互验证复相关系数q2=0.801,外部验证复相关系数Qext2=0.959;采用基于片段的药物设计方法 Topomer Search从ZINC数据库中虚拟筛选出3个Ra基团和7个Rb基团,且设计得到21个新化合物.结果表明:该模型不仅稳定性良好,而且具有较强的预测能力;采用Topomer Search技术能够有效的筛选进而设计出新的化合物,为抗艾滋病新药的设计提供理论依据.     

Comparative molecular field analysis and comparative molecular similarity indices analysis of hydroxyethylamine derivatives as selective human BACE-1 inhibitor

Three-dimensional quantitative structure–activity relationship (3D-QSAR) models were developed based on comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), on a series of 43 hydroxyethylamine derivatives, acting as potent inhibitors of β-site amyloid precursor protein (APP) cleavage enzyme (BACE-1). The crystal structure of the BACE-1 enzyme (PDB ID: 2HM1) with one of the most active compound 28 was available, and we assumed it to be the bioactive conformation of the studied series, for 3D-QSAR analysis. Statistically significant 3D-QSAR model was established on a training set of 34 compounds, which were validated by a test set of 9 compounds. For the best CoMFA model, the statistics are, r ² =  0.998, r²_cv = 0.810{r^{2}_{\rm cv} = 0.810} , n =  34 for the training set and r²_pred = 0.934{r^{2}_{\rm pred} = 0.934} , n = 9 for the test set. For the best CoMSIA model (combined steric, electrostatic, hydrophobic, and hydrogen bond donor fields), the statistics are r ² =  0.978, r²_cv = 0.754{r^{2}_{\rm cv} = 0.754} , n =  34 for the training set and r²_pred = 0.750{r^{2}_{\rm pred} = 0.750} , n =  9 for the test set. The resulting contour maps, produced by the best CoMFA and CoMSIA models, were used to identify the structural features relevant to the biological activity in this series of analogs. The data generated from the present study will further help to design novel, potent, and selective BACE-1 inhibitors.     

De novo design of N-(pyridin-4-ylmethyl)aniline derivatives as KDR inhibitors: 3D-QSAR, molecular fragment replacement, protein-ligand interaction fingerprint, and ADMET prediction

Vascular endothelial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as promising targets for novel anticancer agents. To achieve new potent inhibitors of KDR, we conducted molecular fragment replacement (MFR) studies for the understanding of 3D-QSAR modeling and the docking investigation of arylphthalazines and 2-((1H-Azol-1-yl)methyl)-N-arylbenzamides-based KDR inhibitors. Two favorable 3D-QSAR models (CoMFA with q ², 0.671; r ², 0.969; CoMSIA with q ², 0.608; r ², 0.936) have been developed to predict the biological activity of new compounds. The new molecular database generated by MFR was virtually screened using Glide (docking) and further evaluated with CoMFA prediction, protein?Cligand interaction fingerprint (PLIF) and ADMET analysis. 44 N-(pyridin-4-ylmethyl)aniline derivatives as novel potential KDR inhibitors were finally obtained. In this paper, the work flow developed could be applied to de novo drug design and virtual screening potential KDR inhibitors, and use hit compounds to further optimize and design new potential KDR inhibitors.