Two- and three-dimensional QSAR studies on benzyl amide-ketoacid inhibitors of HIV integrase and their reduced analogues

The discovery of clinically relevant inhibitors of HIV-integrase for antiviral therapy has proven to be a challenging task. Ketoacid-derived inhibitors selectively inhibit the strand transfer reaction of HIV-integrase. To elucidate the structural properties required for HIV-integrase inhibitory activity, we present here the results of two- and three-dimensional (2D and 3D) QSAR studies of a series of 24 benzyl amide derivatives. The 2D-QSAR studies were performed using three methods: the multiple linear regression method (MLR), giving r ² = 0.9340 and q ² = 0.8471; the partial least squares method, with r ² = 0.9291 and q ² = 0.7896; and principle component analysis, giving r ² = 0.6496 and q ² = 0.3893. The 3D-QSAR studies were performed using the stepwise variable selection k-nearest-neighbor molecular field analysis approach; a leave-one-out cross-validated correlation coefficient (q ²) of 0.4577 and a non-cross-validated correlation coefficient (r ²) of 0.5836 were obtained. Contour maps using this approach showed that steric effects dominantly determine binding affinities. The information rendered by 2D and 3D QSAR models may lead to a better understanding of structural requirements of HIV-integrase inhibitors and can help in the design of novel potent molecules.     

Pharmacophore based 3D-QSAR study of VEGFR-2 inhibitors

The growth and metastasis of solid tumors are dependent on angiogenesis. The vascular endothelial growth factor (VEGF) is of particular interest since it is essential for angiogenesis. The development of novel inhibitors of VEGF receptor type 2 (VEGFR-2) is important. Quantitative structure–activity relationship (QSAR) studies were performed to understand the structural factors affecting inhibitory potency of 4-aryl-5-cyano-2-aminopyrimidines. Pharmacophore models indicate that the importance of steric and hydrogen bond acceptor groups. The best-fitted pharmacophore-based alignment was used for comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Both CoMFA (q ² = 0.62, r ² = 0.87, and r ² _predictive = 0.7) and CoMSIA (q ² = 0.54, r ² = 0.86, and r ² _predictive = 0.61) gave reasonable results. Factors such as steric bulkiness, electrostatic effect, and hydrogen bond acceptor were found to be important for the inhibitory activity. It is suggested that negatively charged, bulky H-bond accepting groups around the piperazine nitrogen would enhance inhibition against VEGFR-2.     

3D-QSAR CoMFA and CoMSIA studies on a set of diverse ��1a-adrenergic receptor antagonists

The α-adrenergic receptors (α-ARs) modulate a number of intracellular processes and among these α_1a-adrenergic receptors play an important role in the regulation of physiological processes related to cardiovascular system. In view of its therapeutic potential, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were performed on a set of diverse α-AR antagonists to understand the structural factors affecting their antagonistic activity where both CoMFA (q _train² = 0.709, r _train² = 0.962, and r _predictive² = 0.629) and CoMSIA (q _train² = 0.648, r _train² = 0.949, and r _predictive² = 0.656) models gave statistical significant results. The generated CoMFA and CoMSIA models suggest that steric, electrostatic and hydrophobic interactions play an important role in describing the variation in antagonistic activity. Therefore, the models may be useful in the identification and optimization of novel scaffolds with potent α_1a-adrenergic receptor antagonistic activity.     

QSAR analysis on PfPK7 inhibitors using HQSAR, CoMFA, and CoMSIA

Plasmodium falciparum protein kinase 7 (PfPK7) is an important drug target for the development of anti-malarial treatment. In this study, hologram quantitative structure–activity relationship (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of imidazopyridazine derivatives of PfPK7 inhibitors. The best HQSAR model was obtained using atoms, connection, donor, and acceptor as fragment distinction parameter with fragment size (4–7) using a hologram length of 353 and 6 components (q ² = 0.770, r ² = 0.964). The receptor-guided alignment has produced better statistical results for both CoMFA (q ² = 0.590, r ² = 0.986) and CoMSIA (q ² = 0.735, r ² = 0.988). The predictive ability of the developed models was further validated by a test set of eight compounds. HQSAR contribution map identified the presence of phenyl ring and cyclohexane moiety makes positive contribution for activity. Furthermore, CoMFA and CoMSIA contour maps suggested that additional bulky groups in cyclohexane moiety would increase the biological activity of PfPK7 inhibitors. Finally, these QSAR models were used to design new virtual molecules for imidazopyridazine derivatives and the results obtained from this study could be useful for further investigations.     

Quantitative structure activity relationship (QSAR) studies of a series of 2,5-disubstituted-1,3,4-oxadiazole derivatives as biologically potential compounds

2,5-disubstituted-1,3,4-oxadiazoles and its derivatives are reported to possess wide spectrum of biological activities ranging from antibacterial, antiviral, anti-Parkinson, anti-HIV, and anti-inflammatory activity. Quantitative structure activity relationship (QSAR) analysis is generally carried out to study quantitative relationship between the physicochemical parameters and the biological activity. In the present study, QSAR studies for a set of some disubstituted-1,3,4-oxadiazole derivatives were conducted using TSAR 3.3 software. The in vitro antibacterial activity (MICs) of the compounds against S. aureus and E. coli exhibited good correlation (n = 36, r ² > 0.7, r _cv² > 0.5, s = 0.069) with the prediction made by the model. It was found that the polarizability, thermodynamic, and lipophilicity were major responsible factors for exhibiting the activity.     

噻唑-吡唑啉衍生物作为EGFR抑制剂的3D-QSAR和分子对接模型研究

目的 建立EGFR抑制剂结构和活性之间的关系模型,基于对分子活性产生影响的重要结构性因素的信息,设计新的抑制剂分子并预测其活性,为抑制剂分子的设计提供依据。方法 使用Discovery Studio 2019软件进行3D-QSAR的研究以及偏最小二乘的计算;利用Autodock进行分子对接;使用LigPlot研究二维相互作用。结果 模型具有较高的q²(0.521),和r²(r²_training=0.993,r²_test=0.916,r²_blind=0.940),表明模型具有较高的预测能力和拟合能力。结论 预测结果表明,新设计的化合物活性较高,为EGFR抑制剂分子的设计提供了参考。     

Hansch analysis of novel pyrimidine derivatives as highly potent and specific COX-2 inhibitors

A QSAR study on novel Pyrimidine derivatives as specific COX-2 inhibitory agents was performed with 69 (59 training + 10 test) compounds. Molecular modeling studies were performed using chemoffice 6.0 supplied by cambridgesoft. The sketched structures were subjected to energy minimization and the lowest energy structure was used to calculate the physiochemical properties. The regression analysis was carried out using a computer program called SYSTAT 10.2. The best models were selected from the various statistically significant equations. The study revealed that the hydrogen bond donar groups at position-4 enhances the activity, electron with-drawing groups at position-2 reduces the activity, electron donating groups at position-6 enhances the activity. The analysis resulted in QSAR equation, which suggests that, n = 59, r = 0.957, r ² = 0.915, adjusted squared multiple R = 0.901, Standard error of estimate(s) = 0.294 & validated r ²(q ²) = 0.642. This study can help in rational drug design and synthesis of new selective cyclooxygenase-2 inhibitor with predetermined affinity.     

Molecular Docking and QSAR Studies on Substituted Acyl(thio)urea and Thiadiazolo [2,3-α] Pyrimidine Derivatives as Potent Inhibitors of Influenza Virus Neuraminidase

Surflex–Dock was employed to dock 36 thiourea and thiadiazolo [2,3-α] pyrimidine derivatives into neuraminidase 1a4g. Molecular docking results showed that hydrogen bonding, electrostatic, and hydrophobic features were important factors affecting inhibitory activities of these neuraminidase inhibitors. Moreover, there was a significant correlation between the predicted binding affinity (total scores) and experimental pIC₅₀ values with correlation coefficient r = 0.846 and p < 0.0001. Hologram quantitative structure–activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis were used to develop quantitative structure–activity relationship models. Squared multiple correlation coefficients (r ²) of hologram quantitative structure–activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis models were 0.899, 0.878, and 0.865, respectively. Squared cross-validated correlation coefficient (q ²) of hologram quantitative structure–activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis models was in turn 0.628, 0.656, and 0.509. In addition, squared multiple correlation coefficients for test set (r ²_test) of hologram quantitative structure–activity relationship, comparative molecular field analysis, and comparative molecular similarity indices analysis models were 0.558, 0.667, and 0.566, respectively. The most active sample ID 2 was taken as a template molecule to design new molecules. Based on the comparative molecular field analysis model, new compounds were designed by LeapFrog. Seven new compounds with improved binding energy and predicted activities were finally obtained.     

Insight into structural requirements of antiamoebic flavonoids: 3D‐QSAR and G‐QSAR studies

Plant‐based flavonoids have been found to exhibit strong inhibitory capability against Entamoeba histolytica. So, various QSAR models have been developed to identify the critical features that are responsible for the potency of these molecules. 3D‐QSAR analysis using k‐nearest neighbour molecular field analysis via stepwise forward–backward variable selection method showed best results for both internal and external predictive ability of the model (i.e., q² = 0.64 and pred_r² = 0.56). Also, a group‐based QSAR (G‐QSAR) model was developed based on partial least squares regression combined with stepwise forward–backward variable selection method. It gave best parametric results (r² = 0.74, q² = 0.56 and pred_r² = 0.54) which implied that the model is highly predictive. 3D‐QSAR established that presence/absence of bulk near rings B and C is important in deciding the inhibitory potential of these molecules. Additionally, G‐QSAR provided site‐specific clue wherein modifications related to molecular weight, electronegativity and separation of an oxygen atom in rings A and C can result in enhanced biological activity. To the best of the author's knowledge, this is the first QSAR study of antiamoebic flavonoids, and therefore, we expect the results to be useful in the design of more potent antiamoebic inhibitors.     

Theoretical Characterization of Galanin Receptor Type 3 (Gal3) and Its Interaction with Agonist (GALANIN) and Antagonists (SNAP 37889 and SNAP 398299): An In Silico Analysis

In this study, we report on modeling of galanin receptor type 3 and its interaction with agonist and antagonists using in silico methodologies. Comparative structural modeling of galanin receptor type 3 was based on multiple templates. With the availability of reported selective galanin receptor type 3 antagonists, docking was carried out into the predicted binding site. Similarly, galanin, a reported agonist, was also modeled and then docked into the receptor's active site. CoMFA models were developed using ligand‐based (q² = 0.537, r² = 0.961, noc = 5), and receptor‐guided (docked mode 1: q² = 0.574, r² = 0.946, noc = 5), (docked mode 2: q² = 0.499, r² = 0.954, noc = 5) alignment schemes. CoMFA contour analysis revealed that bulky substitution around the meta position of the phenyl ring, as well as optimal substitution (para) of the phenyl ring, could produce molecules with improved activity. We also found that Gln79, Ile82, Asp86, Trp88, His99, Ile102, Tyr103, Glu170, Pro174, Ala175, Asp185, Arg273, His277, and Tyr281 are crucial, and mutational studies on these residues could be helpful. The results obtained from this study can further be exploited for structure‐based drug design and also help the researchers to identify novel antagonists targeting galanin receptor type 3.     

新型甲型H1N1流感病毒神经氨酸酶抑制剂细胞水平评价体系的建立

本文旨在建立以2009新型甲型H1N1流感病毒神经氨酸酶 (neuraminidase, NA) 为靶点的神经氨酸酶抑制剂 (neuraminidase inhibitors, NAIs) 细胞水平评价体系。NA有促进流感病毒释放的作用, 本研究应用重组病毒技术, 通过将表达NA [A/California/04/2009 (H1N1)] 的质粒、表达流感病毒血凝素蛋白HA (hemagglutinin) 的质粒以及表达敲除外壳基因并带有荧光素酶报告基因的HIV-1基因组共转染至病毒生成细胞, 产生以HA、NA为外壳蛋白包裹HIV-1核心的重组病毒。在病毒释放前加入不同浓度的化合物, 收集病毒上清液感染细胞, 通过测定感染率来反映化合物对重组病毒NA的抑制作用。经用阳性对照药物奥司他韦及其羧酸盐证实本模型能够反映化合物对病毒NA的抑制作用; 在此基础上, 本研究还建立了奥司他韦耐药株评价模型。本研究所建立的体系可用于针对新型甲型H1N1流感病毒及其临床耐药株神经氨酸酶抑制剂的寻找和评价。