3D-QSAR study of pyrrolidine derivatives as matrix metalloproteinase-2 inhibitors

In order to develop potent inhibitors of matrix metalloproteinase-2(MMP-2) as anticancer agents, a three-dimensional quantitative structure–activity relationship (3D-QSAR) model was established by using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. This study correlates the MMP-2 inhibitory activities of 67 pyrrolidine derivatives to steric, electrostatic, hydrophobic, and hydrogen-bond donor and acceptor fields. After using two different molecular alignments, both CoMFA and CoMSIA models resulted in good statistical predictions, a case in point being their high q ² values of between 0.757 and 0.843. The CoMFA and CoMSIA models established herein will be helpful in understanding the structure–activity relationship of pyrrolidine derivatives as well as in the design of novel derivatives with enhanced MMP-2 inhibitory activity.     

Quantitative Structure Activity Relationship between Diazabicyclo[4.2.0]octanes Derivatives and Nicotinic Acetylcholine Receptor Agonists

Three dimensional quantitative structure activity relationship between diazabicyclo[4.2.0]octanes and nicotinic acetylcholine receptor (hα4β2 and hα3β4) agonists was studied using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). From 11 CoMFA and CoMSIA models, CoMSIA with steric and electrostatic fields gave the best predictive models (q²=0.926 and 0.945, r²_ncv=0.983 and 0.988). This study can be used to develop potent hα4β2 receptor agonists with low activity on hα3β4 subtype.     

Pharmacophore-based 3D-QSAR of HIF-1 inhibitors

(Aryloxyamino)benzoic acids and nicotinic/isonicotinic acids represent an important new class of small molecules that inhibit the activation of Hypoxia-Inducible Factor (HIF)-1. In order to understand the factors affecting inhibitory potency of HIF-1 inhibitors, 3 dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed. Since no receptor structure are available, the pharmacophore-based alignment was used for comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The CoMFA and CoMSIA models gave reasonable statistics (CoMFA: q² = 0.564, r²=0.945; CoMSIA: q² = 0.575, r²=0.929). Both CoMFA and CoMSIA results indicate that the steric interaction is a major factor, while CoMSIA suggests importance of hydrogen bonding. These findings about steric and H-bonding effects can be useful to design new inhibitors. Equally contributed in this work.     

喹啉酮类小分子p53-MDM2结合抑制剂3D-QSAR研究

目的 设计、合成高活性的小分子p53-MDM2结合抑制剂,建立具有预测能力的3D-QSAR模型。方法 采用分子模拟软件Sybyl,利用比较分子场方法(CoMFA)、比较分子相似性指数法(CoMSIA),选择已报道的具有p53-MDM2结合抑制活性的一类有相同母核的21个异喹啉酮衍生物作为训练集,7个作为预测集进行3D-QSAR模型的建立和验证。结果 模型具有较高q²(q²_CoMFA=0.545,q²_CoMSIA=0.528)和r2(r²_CoMFA=0.984,r²_CoMSIA=0.972)值,表明2组模型具有较高的拟和能力及预测能力。结论 该模型具有较高的预测能力,为设计、合成高活性的小分子p53-MDM2结合抑制剂提供了理论依据。     

Comparative molecular similarity indices analysis of some 1-substituted imidazole analogs as Candida albicans P450-demethylase inhibitors

A three-dimensional quantitative structure–activity relationship of 66 structurally and functionally diverse series of 1-substituted imidazoles with antifungal activity was studied using the CoMSIA method. The compounds were divided into a training set of 56 molecules and a test set of 10 molecules. The optimum CoMSIA model obtained for the training set were all statistically significant with cross-validated coefficients (q ²) of 0.725 and conventional coefficients (r _ncv ² ) of 0.998. The predictive ability of CoMSIA was determined using a test set of ten imidazole derivatives. CoMSIA model (Model 1) obtained from steric, electrostatic, and H-bond acceptor fields were found to have best predictivity with a predictive correlation coefficient (r _pred ² ) of 0.60. Based upon the information derived from CoMSIA, it is evident that steric, electrostatic, and hydrogen bond acceptor groups may be important for the design of more potent imidazole analogs as potent Candida P450DM inhibitors.     

新型二芳基三嗪类抗锥体虫病化合物的三维定量构效关系研究

目的 建立具有预测能力的新型二芳基三嗪类抗锥体虫病化合物三维定量构效关系（3D-QSAR）模型。方法 通过对具有抗锥体虫活性的二芳基三嗪类化合物库进行结构分析,利用比较分子场分析法（CoMFA）和比较分子相似性指数分析法（CoMSIA）,建立3D-QSAR模型。结果 模型具有较高q²（q_CoMFA²=0.697,q_CoMSIA²=0.561）和r²（r_CoMFA²=0.998,r_CoMSIA²=0.966）值,表明2组模型具有较高的拟和能力及预测能力。结论 建立的CoMFA和CoMSIA模型均具有良好的预测能力,为设计更高活性的新型二芳基三嗪类抗锥体虫病化合物提供了理论依据和研究方向。     

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 and molecular docking studies of hydroxamic acids as peptide deformylase inhibitors

Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and molecular docking study were conducted on hydroxamic acids as potent peptide deformylase (PDF) inhibitors. The optimal CoMFA model gave statistically significant results with q ² and r ² values of 0.568 and 0.956, respectively. The optimal CoMSIA model with combination of steric, hydrophobic and H-bond donor fields resulted in the best results with q ² and r ² values of 0.722 and 0.958, respectively. These two models were validated by an external test set of eight compounds with satisfactory predictive r ² values of 0.810 and 0.820, respectively. The contour plots of molecular fields indicated that electrostatic and bulky groups substituted at the R₁ position, and electropositive and small substituted at R₂ position were favorable for the inhibitory activity. In addition, FlexX docking was employed to investigate the binding mode between PDF and its inhibitors. It was found that hydrogen bond interactions might be an important factor for binding affinity of inhibitors in the hydrophobic cavity. Based on the optimal CoMSIA model and FlexX docking, a series of PDF inhibitors with high predictive activities have been designed. This work might provide valuable information in designing more promising PDF inhibitors.     

Comparative molecular similarity indices analysis for predicting the antihyperglycemic activity of thioglitazones

The three-dimensional (3D) quantitative structure-activity relationship method, comparative molecular similarity indices analysis (CoMSIA), was applied to a series of thioglitazones (or thiazolidinediones) to derive structure-activity relationships of their antihyperglycemic activity. Evaluation of 39 aligned compounds from the training set served to establish the various models. Predictions were obtained with CoMSIA steric, electrostatic, hydrophobic, and hydrogen bond acceptor and donor fields (q ² = 0.51, r ² = 0.942). The developed model was validated by an external test set of 11 compounds, with the satisfactory predictive r ² value of 0.849. The information obtained from the present CoMSIA 3D contour map can be utilized for the design of specific PPAR-γ agonists.     

A comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) of anthranilamide derivatives that are multidrug resistance modulators

In a continuing effort to develop potent and selective modulators of P-glycoprotein (P-gp) activity overcoming the chemoresistance acquired by tumor cells during cancer chemotherapy, we developed 3D quantitative structure-activity relationship (3D QSAR) models using CoMFA and CoMSIA analyses. This study correlates the P-glycoprotein inhibitory activities of 49 structurally related anthranilamide derivatives to several physicochemical parameters representing steric, electrostatic, acceptor, donor, and hydrophobic fields. Both CoMFA and CoMSIA models using three different alignment conformations gave good internal predictions, and their cross-validated r2 values are between 0.503 and 0.644. These most comprehensive CoMFA and CoMSIA models are useful in understanding the structure-activity relationships of anthranilamide derivatives as well as aid in the design of novel derivatives with enhanced modulation of P-gp activity.     

Molecular modeling study of HIV-1 gp120 attachment inhibitors

The viral glycoprotein 120 (gp120) is a glycoprotein exposed on viral surface. The gp120 is essential for virus entry into cells as it plays a vital role in seeking out specific cell surface receptors for entry. In this article, we performed docking and three-dimensional quantitative structure activity relationship (3D-QSAR) study on a series of 48 indole glyoxamide derivatives as gp120 inhibitors. Docking study revealed that the inhibitor docked deeply into the gp120 cavity rather than Phe43 of cluster of differentiation 4 (CD4). 3D-QSAR methodologies, comparative molecular field analysis (CoMFA), and comparative molecular similarity indices analysis (CoMSIA) were utilized to rationalize the structural variations with their inhibitory activities. The docked pose of the most potent molecule (43) was used to determine the structures of other molecules. The CoMFA yielded a model with cross-validated correlation coefficient of (q ²) 0.73 and non-cross-validated correlation coefficient of (r ²) 0.89 with optimum number of components (N?=?3). The CoMSIA models were obtained with the combination of various parameters. Final model was computed with steric, hydrophobic- and hydrogen-bond acceptor (SHA) parameters with reasonable statistics (q ²?=?0.80, r ²?=?0.94 and N?=?5). The predictive power of developed CoMFA and CoMSIA models were assessed by test set (nine molecules). The predictive r _pred ² for CoMFA and CoMSIA model was found to be 0.93 and 0.74, respectively. The generated contour maps were plotted onto the gp120 active site to correlate structural variations with their biological activity in protein environment. Contour map analyses showed the importance of 4-F substitution of indole ring, which made essential electronic interaction with the crucial residue (Trp427). The 3D models could explain nicely the structure–activity relationships of indole glyoxamide analogs. This would give proper guidelines to further enhance the activity of novel inhibitors.     

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.     

CoMFA, ADVANCE CoMFA AND CoMSIA STUDIES ON THE OXADIAZOLE SUBSTITUTED α-ISOPROPOXY PHENYLPROPIONIC ACIDS FOR PPARα AGONISTIC ACTIVITY

The fibrate class drugs effect on lipid metabolism through PPARα receptor. Thus, PPARα/γ dual agonists may provide superior therapy to the current PPARγ-selective agonists, due to the additional lipid control afforded by the PPARα component. In order to elucidate the structural and physicochemical requirements responsible for binding to the PPARα enzyme and to develop predictive models, CoMFA, advance CoMFA and CoMSIA studies have been carried out on a series of oxadiazole-substituted α-isopropoxy phenylpropanoic acids. The CoMFA model developed using most common structure based alignment and tripos standard field demonstrated high predictive ability (q² = 0.573, r² = 0.711). Further the PLS coefficient contour maps from CoMFA and CoMSIA, well explain the structural variation of the PPARα inhibitors.     

3D-QSAR research of curcumin derivatives

Curcumin exhibits a great ability in various biological and pharmacological activities. Evaluation of curcumin derivatives served to establish the three-dimensional quantitative structure–activity relationship (3D-QSAR) model which was validated by the evaluation of a serial of 22 compounds. Two favorable 3D-QSAR models (CoMFA with q ² = 0.539, R ² = 0.981; CoMSIA with q ² = 0.451, R ² = 0.907) had been developed to predict the biological activity of curcumin derivatives, and external metric q _pred ² (CoMFA with 0.79; CoMSIA with 0.78) and r _m ² _(overall) (CoMFA with 0.71; CoMSIA with 0.56) were applied to evaluate the ability of prediction. Comparing the results obtained from both standard models, we found that reducing the carbon chains of curcumin (S2 and A1 zones) could increase its MCF-7 cytotoxicity; exchanging acceptor/donor substituent on A2 and A4, A3 and D3 zones could turnover its cytotoxicity of MCF-7. These results help with understanding the specific activity of curcumin compounds and designing new specific MCF-7 inhibitors.