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．     

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.     

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.     

Structural insights of dipeptidyl peptidase-IV inhibitors through molecular dynamics-guided receptor-dependent 4D-QSAR studies

Dipeptidyl peptidase-IV (DPP-IV) inhibitors are promising antidiabetic agents. Currently, several DPP-IV inhibitors have been approved for therapeutic use in diabetes mellitus. Receptor-dependent 4D-QSAR is comparatively a new approach which uses molecular dynamics simulations to generate conformational ensemble profiles of compounds representing a dynamic state of compounds at a target’s binding site. This work describes a receptor-dependent 4D-QSAR study on triazolopiperazine derivatives. QSARINS multiple linear regression method was adopted to generate 4D-QSAR models. A model with 9 variables was found to have better predictive accuracy with \({R}^{2}=0.692\), \({Q}^{2}\) (leave-one-out) = 0.592 and \({R}^{2}\) predicted = 0.597. The location of these 9 variables at the binding site of DPP-IV revealed the importance of the residues Val711, Tyr662, Tyr666, Val202, Asp200 and Thr199 in making critical interactions with DPP-IV inhibitors. The study of these critical interactions revealed the structural features required in DPP-IV inhibitors. Thus, in this study the importance of a halogen substituent on a phenyl ring, the extent of substitution on the triazolopiperazine ring, the presence of an ionizable amino group and the presence of a hydrophobic substituent that can bind deeper in binding pocket of DPP-IV were revealed.     

苯氨基磺酸衍生物作为HPTPβ抑制剂的Topomer CoMFA研究及分子对接

采用Topomer CoMFA方法对27个苯氨基磺酸衍生物进行了三维定量构效关系研究。得到了3D-QSAR模型，其交互验证系数q2为0.713，非交叉相关系数r2为0.995，主成分数N为6，标准估计误差SEE为0.183。结果表明该模型有较好的预测能力。采用Topomer Search技术在ZINC数据库中进行R基的筛选，并设计了5个新分子，最后用分子对接技术研究了新分子与苯氨基磺酸衍生物大分子蛋白的作用模式，从结果中可以看到，新分子与苯氨基磺酸衍生物蛋白的A/ARG220、A/ARG63和A/ASN115位点作用显著.     

基于Topomer CoMFA的吡唑啉嘧啶类IL-2诱导T细胞激酶抑制剂的3D-QSAR及分子对接

本文采用Topomer CoMFA方法对37个吡唑啉嘧啶类IL-2诱导T细胞激酶抑制剂进行三维定量构效关系研究.得到3D-QSAR模型,其交互验证系数q~2为0.728,非交叉相关系数r~2为0.994,主成分数N为8,标准估计误差SEE为0.097.结果表明该模型有较好的预测能力.采用Topomer Search技术在ZINC数据库中进行R基的筛选,并设计6个新分子.最后用分子对接技术研究了4个新分子与IL-2诱导的T细胞激酶(Itk)大分子蛋白的作用模式,从结果中可以看到,4个新分子与Itk蛋白的A/LYS391、A/MET438位点作用显著.     

Docking and quantitative structure–activity relationship studies for sulfonyl hydrazides as inhibitors of cytosolic human branched-chain amino acid aminotransferase

We have performed the docking of sulfonyl hydrazides complexed with cytosolic branched-chain amino acid aminotransferase (BCATc) to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 20 compounds with variation in structure and activity. In addition, the predicted inhibitor concentration (IC₅₀) of the sulfonyl hydrazides as BCAT inhibitors were obtained by a quantitative structure–activity relationship (QSAR) method using three-dimensional (3D) vectors. We found that three-dimensional molecule representation of structures based on electron diffraction (3D-MoRSE) scheme contains the most relevant information related to the studied activity. The statistical parameters [cross-validate correlation coefficient (Q ² = 0.796) and fitted correlation coefficient (R ² = 0.899)] validated the quality of the 3D-MoRSE predictive model for 16 compounds. Additionally, this model adequately predicted four compounds that were not included in the training set.     

应用CoMFA研究抗菌肽的三维定量构效关系

采用比较分子场分析(CoMFA)方法对抗菌九肽、抗菌十四肽、抗菌十八肽进行三维定量构效关系(3D-QSAR)分析,建立了预测力强的3D-QSAR模型.结果表明,所建立的3D-QSAR模型对三种抗菌肽均有较好的统计学稳定性及预测能力(抗菌九肽:交叉验证系数q~2=0.537,非交叉验证相关系数r~2=0.961,外部样本检验复相关系数Q_(ext)~2=0.883;抗菌十四肽:q~2=0.502,r~2=0.718,Q_(ext)~2=0.645;抗菌十八肽:q~2=0.550,r~2=0.967,Q_(ext)~2=0.989).三维等势图不仅解释了抗菌肽结构与活性的关系,而且为抗菌肽的进一步合成提供了理论依据.     

Insights into the interaction and binding mode of a set of antifungal azoles as inhibitors of potential fungal enzyme-based targets

Azole-containing compounds are a kind of chemical entities of natural and synthetic origin having a wide-range of activities. They are therefore considered as important moieties for fungicide development, mostly due to the possible action on several enzyme-based targets. As part of our research on fungicidal agents, the relationship between the ligand-enzyme affinities of several synthetic azole-containing compounds against a set of fungal enzyme-based targets was in silico evaluated through molecular docking. The affinity values of the test compounds were mostly higher than those of the respective test controls. Binding modes between enzymes and test compounds were firstly investigated through Vina scores and ligand–residue interactions. Furthermore, statistically relationships among docking scores were successfully found by multivariate analysis. They were mostly correlated with reported MIC₈₀ values, so it denoted an evident discrimination of the test compounds. Strong electron withdrawing groups on phenylacrylamide moiety were responsible for establishing stronger complexes with the enzyme targets, being trichodiene synthase and α-l-fucosidase the most important ones. Moreover, stability of a set of representative protein/ligand complexes was also analyzed by 10 ns molecular dynamics simulations (MD). Significant differences into the MD runs were detected and directly correlated to docking performances. Finally, docking affinity scores and HOMO–LUMO energy gaps resulted well predicted by comparative molecular field analysis (CoMFA) models, demonstrating the structure type is particularly associated with those calculated properties and these results were thus consistent with the respective validation parameters.     

Synthesis, in vitro antitubercular activity and 3D-QSAR study of 1,4-dihydropyridines

In continuation of our research program on new antitubercular agents, this article is a report of the synthesis of 97 various symmetrical, unsymmetrical, and N-substituted 1,4-dihydropyridines. The synthesized molecules were tested for their activity against M. tuberculosis H ₃₇Rv strain with rifampin as the standard drug. The percentage inhibition was found in the range 3–93%. In an effort to understand the relationship between structure and activity, 3D-QSAR studies were also carried out on a subset that is representative of the molecules synthesized. For the generation of the QSAR models, a training set of 35 diverse molecules representing the synthesized molecules was utilized. The molecules were aligned using the atom-fit technique. The CoMFA and CoMSIA models generated on the molecules aligned by the atom-fit method show a correlation coefficient (r ²) of 0.98 and 0.95 with cross-validated r ²(q ²) of 0.56 and 0.62, respectively. The 3D-QSAR models were externally validated against a test set of 19 molecules (aligned previously with the training set) for which the predictive ${r^{2} (r^{2}_{\rm pred})}$ is recorded as 0.74 and 0.69 for the CoMFA and CoMSIA models, respectively. The models were checked for chance correlation through y-scrambling. The QSAR models revealed the importance of the conformational flexibility of the substituents in antitubercular activity.     

Parallel synthesis of novel antitumor agents: 1,2,3-triazoles bearing biologically active sulfonamide moiety and their 3D-QSAR

A 60-member 1,2,3-triazoles bearing biologically active sulfonamide moiety library was synthesized via azide–alkyne cycloaddition and examined for cytotoxic activity against human leukemia cell line HL-60. 25 of them were evaluated further in four additional cancer cell lines (HepG2, A549, PC3, SGC7901). Most of the 25 compounds showed moderate cytotoxic activities against the tested cell lines. Furthermore, the structure–activity relationships were discussed and a reliable 3D-QSAR model with good prediction (r_cv² = 0.64, r² = 0.958){\left(r_{\rm cv}^{2 } = 0.64, r^{2} = 0.958\right)} was generated on the basis of our synthesized 1,2,3-triazoles for their cytotoxic activities against the HL-60 cell line. The contour map of the CoMFA should aid in the design of new antitumor agents.     

3D-QSAR and molecular docking analysis of biphenyl amide derivatives as p38α mitogen-activated protein kinase inhibitors

The p38α mitogen-activated protein (MAP) kinase plays a vital role in treating many inflammatory diseases such as rheumatoid arthritis, inflammatory bowel disease, Crohn's disease and psoriasis. Herein, we have performed 3D-QSAR and molecular docking analysis on a novel series of biphenyl amides to design potent p38 MAP kinase inhibitors. This study correlates the p38 MAP kinase inhibitory activities of 80 biphenyl amide derivatives to several stereochemical parameters representing steric, electrostatic, hydrophobic, hydrogen bond donor and acceptor fields. The resulting model from CoMFA and CoMSIA exhibited excellent [Formula: see text] values of 0.979 and 0.942, and [Formula: see text] values of 0.766 and 0.748, respectively. CoMFA predicted [Formula: see text] of 0.987 and CoMSIA predicted [Formula: see text] of 0.761 showed that the predicted values were in good agreement with experimental values. Glide (5.5) program gave the path for binding mode exploration between the inhibitors and p38α MAP kinase. We have accordingly designed novel p38α MAP kinase inhibitors by utilizing LeapFrog and predicted with excellent activity in the developed models.     

2D-SAR and 3D-QSAR analyses for acetylcholinesterase inhibitors

Alzheimer’s disease (AD) accounts for almost three quarters of dementia patients and interferes people’s normal life. Great progress has been made recently in the study of Acetylcholinesterase (AChE), known as one of AD’s biomarkers. In this study, acetylcholinesterase inhibitors (AChEI) were collected to build a two-dimensional structure–activity relationship (2D-SAR) model and three-dimensional quantitative structure–activity relationship (3D-QSAR) model based on feature selection method combined with random forest. After calculation, the prediction accuracy of the 2D-SAR model was 89.63% by using the tenfold cross-validation test and 87.27% for the independent test set. Three cutting ways were employed to build 3D-QSAR models. A model with the highest \({q}^{2}\) (cross-validated correlation coefficient) and \({r}^{2 }\)(non-cross-validated correlation coefficient) was obtained to predict AChEI activity. The mean absolute error (MAE) of the training set and the test set was 0.0689 and 0.5273, respectively. In addition, molecular docking was also employed to reveal that the ionization state of the compounds had an impact upon their interaction with AChE. Molecular docking results indicate that Ser124 might be one of the active site residues.