培氟沙星均三唑硫醚衍生物抗肝癌活性的CoMFA模型与分子设计

基于比较分子力场分析(CoMFA)方法建立24种培氟沙星均三唑硫醚衍生物抗肝癌活性(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10个化合物(含模板分子及新设计的5个分子)作为模型验证。已建立的3D-QSAR模型的交叉验证系数(R_cv²)、非交叉验证系数(R^₂)分别为0.705、0.940,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74.8%、25.2%,表明影响抗肝癌活性(pM)的主要因素是取代基的疏水性和空间契合,其次是库仑力、氢键及配位。基于三维等势图,设计了5个具有较高抗肝癌活性的分子,有待医学实验验证。     

Docking-based 3D-QSAR study of pyridyl aminothiazole derivatives as checkpoint kinase 1 inhibitors

Checkpoint kinase 1 (Chk1) is a promising target for the design of novel anticancer agents. In the present work, molecular docking simulations and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on pyridyl aminothiazole derivatives as Chk1 inhibitors. AutoDock was used to determine the probable binding conformations of all the compounds inside the active site of Chk1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed based on the docking conformations and alignments. The CoMFA model produced statistically significant results with a cross-validated correlation coefficient (q²) of 0.608 and a coefficient of determination (r²) of 0.972. The reliable CoMSIA model with q² of 0.662 and r² of 0.970 was obtained from the combination of steric, electrostatic and hydrogen bond acceptor fields. The predictive power of the models were assessed using an external test set of 14 compounds and showed reasonable external predictabilities (r²_pred) of 0.668 and 0.641 for CoMFA and CoMSIA models, respectively. The models were further evaluated by leave-ten-out cross-validation, bootstrapping and progressive scrambling analyses. The study provides valuable information about the key structural elements that are required in the rational design of potential drug candidates of this class of Chk1 inhibitors.     

Pharmacophore interactions analysis and prediction of inhibitory activity of 1,7-diazacarbazoles as checkpoint kinase 1 inhibitors: application of molecular docking, 3D-QSAR and RBF neural network

In the present study, we mainly focused on new synthesized 1,7-diazacarbazole derivatives (44 active molecules) as Chk1 inhibitors to build 3D-QSAR model. Comparative molecular field analysis (CoMFA) model with three principal components was developed. The relative contributions in building of CoMFA model were 64.41 % for steric field and 35.59 % for electrostatic field. R ² values for training and test sets of CoMFA model were 0.8724 and 0.7818, respectively, and squared correlation coefficient for leave-one-out cross-validation test (q ²) was 0.6753. To improve the predictive power, a new 3D-QSAR model was developed by using radial basis function network (RBFN) and score of CoMFA interactions energy values as input variables. Scores 1, 2 and 3 were used as input variables, and a RBFN model with seven centers and spread value equal to 95 was developed to create a nonlinear 3D-QSAR model. R ² values for training and test sets were 0.9613 and 0.8564, and q ² for leave-one-out cross-validation test was 0.9258. Docking of all molecules to 3DX ligand binding site of Chk1 receptor indicated six interactions as pharmacological interactions between compounds and binding site of receptors. These pharmacological interactions were hydrogen bonding with LEU-15 and GLU-85 in main chain and four van der Waals interactions with LEU-15, VAL-23, TYR-86 and LEU-137 in side chain. CoMFA contour plots were used to design new inhibitors, and inhibitory activity of each compound was predicted by using CoMFA and RBFN models.     

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.     

Docking-based CoMFA and CoMSIA study of azaindole carboxylic acid derivatives as promising HIV-1 integrase inhibitors

Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed based on a series of azaindole carboxylic acid derivatives that had previously been reported as promising HIV-1 integrase inhibitors. Docking studies to explore the binding mode were performed based on the highly active molecule 36. The best docked conformation of molecule 36 was used as template for alignment. The comparative molecular field analysis (CoMFA) model (including steric and electrostatic fields) yielded the cross validation q ² = 0.655, non-cross validation r ² = 0.989 and predictive r ² _pred = 0.979. The best comparative molecular similarity indices analysis (CoMSIA) model (including steric, electrostatic, hydrophobic and hydrogen-bond acceptor fields) yielded the cross validation q ² = 0.719, non-cross validation r ² = 0.992 and predictive r ² _pred = 0.953. A series of new azaindole carboxylic acid derivatives were designed and the HIV-1 integrase inhibitory activities of these designed compounds were predicted based on the CoMFA and CoMSIA models.     

Comparison of Estrogen Receptor α and β Subtypes Based on Comparative Molecular Field Analysis (CoMFA)

Abstract

A substantial body of evidence indicates that both humans and wildlife suffer adverse health effects from exposure to environmental chemicals that are capable of interacting with the endocrine system. The recent cloning of the estrogen receptor β subtype (ER-β) suggests that the selective effects of estrogenic compounds may arise in part by the control of different subsets of estrogen-responsive promoters by the two ER subtypes, ER-α and ER-β. In order to identify the structural prerequisites for ligand-ER binding and to discriminate ER-α and ER-3 in terms of their ligand-binding specificities, Comparative Molecular Field Analysis (CoMFA) was employed to construct a three-dimensional Quantitative Structure-Activity Relationship (3D-QSAR) model on a data set of 31 structurally-diverse compounds for which competitive binding affinities have been measured against both ER-α and ER-β. Structural alignment of the molecules in CoMFA was achieved by maximizing overlap of their steric and electrostatic fields using the Steric and Electrostatic ALignment (SEAL) algorithm. The final CoMFA models, generated by correlating the calculated 3D steric and electrostatic fields with the experimentally observed binding affinities using partial least-squares (PLS) regression, exhibited excellent self-consistency (r ² > 0.99) as well as high internal predictive ability (q ² > 0.65) based on cross-validation. CoMFA-predicted values of RBA for a test set of compounds outside of the training set were consistent with experimental observations. These CoMFA models can serve as guides for the rational design of ER ligands that possess preferential binding affinities for either ER-α or ER-β. These models can also prove useful in risk assessment programs to identify real or suspected EDCs.     

Pharmacophore Screening on Piperidinecarboxamides Derivatives Based on GALAHAD and CoMFA Models

In order to discover the novel anticonvulsant drugs, pharmacophore screening of the anticonvulsant inhibitors was enforced. Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets (GALAHAD) and Comparative Molecular Field Analysis (CoMFA) studies were combined to implement our research. Firstly, multiple models were generated using GALAHAG based on high active molecules. Secondly, several of them were validated using the CoMFA study. Finally, a good values of q² from training set and promising predictive power from test set were obtained based on one model simutaneously. One model had been selected as the most reasonable pharmacophore model. The results of the CoMFA study based on the model 1 suggested that both steric and electrostatic interactions played important roles.     

Adenosine A 2A receptor agonists: CoMFA-based selection of the most predictive conformation

A step-wise comparative molecular field analysis (CoMFA)-based procedure was applied to a series of 51 2-oxyadenosines in order to select the most predictive conformation for binding to A 2A adenosine receptor (AR). The highest correlation and predictive power were found for conformers with side chain at 2nd position oriented in the direction opposite to the exocyclic amino group on the adenine ring (torsion N1C2OR=120 ) and fully extended. The interaction of ligand and receptor is under steric and electrostatic control. The steric contribution is of a greater importance for the predictivity than the electrostatic one. Hydrophobicity of the compounds investigated does not affect significantly either the affinity to A 2A AR, nor the predictivity of the models.     

Molecular Modeling and Design of Arylthioindole Derivatives as Tubulin Inhibitors

Three-dimensional quantitative structure activity relationship （3D-QSAR） and docking studies of a series of arylthioindole derivatives as tubulin inhibitors against human breast cancer cell line MCF-7 have been carried out. An optimal 3D-QSAR model from the comparative molecular field analysis （CoMFA） for training set with significant statistical quality （R2=0.898） and predictive ability （q2=0.654） was established. The same model was further applied to predict pIC50 values of the compounds in test set, and the resulting predictive correlation coefficient R2（pred） reaches 0.816, further showing that this CoMFA model has high predictive ability. Moreover, the appropriate binding orientations and conformations of these compounds interacting with tubulin are located by docking study, and it is very interesting to find the consistency between the CoMFA field distribution and the 3D topology structure of active site of tubulin. Based on CoMFA along with docking results, some important factors improving the activities of these compounds were discussed in detail and were summarized as follows： the substituents R3-R5 （on the phenyl ring） with higher electronegativity, the substituent R6 with higher eleetropositivity and bigger bulk, the substituent R7 with smaller bulk, and so on. In addition, five new compounds with higher activities have been designed. Such results can offer useful theoretical references for experimental works.     

A ligand-based comparative molecular field analysis (CoMFA) and homology model based molecular docking studies on 3′, 4′-dihydroxyflavones as rat 5-lipoxygenase inhibitors: Design of new inhibitors

In this study, ligand based comparative molecular field analysis (CoMFA) with five principal components was performed on class of 3′, 4′-dihydroxyflavone derivatives for potent rat 5-LOX inhibitors. The percentage contributions in building of CoMFA model were 91.36% for steric field and 8.6% for electrostatic field. R² values for training and test sets were found to be 0.9320 and 0.8259, respectively. In case of LOO, LTO and LMO cross validation test, q² values were 0.6587, 0.6479 and 0.5547, respectively. These results indicate that the model has high statistical reliability and good predictive power. The extracted contour maps were used to identify the important regions where the modification was necessary to design a new molecule with improved activity. The study has developed a homology model for rat 5-LOX and recognized the key residues at the binding site. Docking of most active molecule to the binding site of 5-LOX confirmed the stability and rationality of CoMFA model. Based on molecular docking results and CoMFA contour plots, new inhibitors with higher activity with respect to the most active compound in data set were designed.     

High-affinity interactions of ligands at recombinant Guinea pig 5HT7 receptors

The serotonin 5HT₇ receptor has been implicated in numerous physiological and pathological processes from circadian rhythms [1] to depression and schizophrenia. Clonal cell lines heterologously expressing recombinant receptors offer good models for understanding drug-receptor interactions and development of quantitative structure-activity relationships (QSAR). Comparative Molecular Field Analysis (CoMFA) is an important modern QSAR procedure that relates the steric and electrostatic fields of a set of aligned compounds to affinity. Here, we utilized CoMFA to predict affinity for a number of high-affinity ligands at the recombinant guinea pig 5HT₇ receptor. Using R-lisuride as the template, a final CoMFA model was derived using procedures similar to those of our recent papers [2, 3, 4] The final cross-validated model accounted for >85% of the variance in the compound affinity data, while the final non-cross validated model accounted for >99% of the variance. Model evaluation was done using cross-validation methods with groups of 5 ligands. Twenty cross-validation runs yielded an average predictive r²(q²) of 0.779 ± 0.015 (range: 0.669–0.867). Furthermore, 3D-chemical database search queries derived from the model yielded hit lists of promising agents with high structural similarity to the template. Together, these results suggest a possible basis for high-affinity drug action at 5HT₇ receptors.     

Three-dimensional quantitative structure-activity relationship of 4,5,6,7-tetrahydrothienopyridines analogues as glucose-6-phosphatase inhibitors

A Three-Dimensional Quantitative Structure-activity Relationship (3D-QSAR) model that correlates the biological activities with the chemical structures of a series of Glucose-6-phosphatase inhibitors, exemplified by the 4,5,6,7-tetrahydrothienopyridines derivatives, was established by means of comparative molecular field analysis (CoMFA). The resulting leave-one-out cross-validated value (q²=0.600) and non-cross-validated value (r²=0.956) indicate that the obtained pharmacophore model indeed mimics the steric and electrostatic environment, where inhibitors bind to the enzyme. Furthermore, the developed model also possesses promising predictive ability as discerned by the testing on the external test set. The analysis of the CoMFA contour map, which reveal how steric and electrostatic interactions contribute to inhibitors' bioactivities, provide us with the important information to understand the molecular nature of inhibitor-enzyme interactions and to aid in the design of more potent Glucose-6-phosphatase inhibitors.     

Pharmacophore modeling, 3D-QSAR,docking, and molecular dynamics simulation on topoisomerase IV inhibitors of wild type Staphylococcus aureus

Staphylococcus aureus is a gram-positive bacterium. It is a foremost cause of skin and respiratory infections, endocarditis, osteomyelitis, Ritter’s disease, and bacteraemia. Topoisomerase enzyme is involved in preventing or correcting topological problems of overwinding or underwinding occurring in DNA before replication process. An exhaustive molecular modeling studies that includes pharmacophore modeling, ligand-based three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, molecular dynamics simulation, and ADME calculations were performed on isothiazoloquinolones derivatives which are reported as effective inhibitors against topoisomerase IV of wild type S. aureus. In pharmacophore modeling by using pharmacophore alignment and scoring engine (PHASE) a five-point model (AHHRR.3) was generated with existing compounds having statistical significant as correlation coefficient (R ² = 0.954), cross-validation coefficient (Q ² = 0.650), and F value of 130.5. Ligand-based 3D-QSAR study was applied using comparative molecular field analysis (CoMFA) with Q ² = 0.616, R ² = 0.989, and comparative molecular similarity indices analysis (CoMSIA) with Q ² = 0.510, R ² = 0.995. The predictive ability of this model was determined using a test set of molecules that gave acceptable predictive correlation (R ² Pred) values 0.55 and 0.56 for CoMFA and CoMSIA, respectively. Docking and molecular dynamic simulations were employed to position the inhibitors into protein active site to find out the most probable binding mode and most reliable conformations. Developed pharmacophore models and docking methods provide guidance to design enhanced activity molecules.

     

Adenosine A2A receptor agonists: CoMFA-based selection of the most predictive conformation

A step-wise comparative molecular field analysis (CoMFA)-based procedure was applied to a series of 51 2-oxyadenosines in order to select the most predictive conformation for binding to A2A adenosine receptor (AR). The highest correlation and predictive power were found for conformers with side chain at 2nd position oriented in the direction opposite to the exocyclic amino group on the adenine ring (torsion N1C2OR = 120 degrees) and fully extended. The interaction of ligand and receptor is under steric and electrostatic control. The steric contribution is of a greater importance for the predictivity than the electrostatic one. Hydrophobicity of the compounds investigated does not affect significantly either the affinity to A2A AR, nor the predictivity of the models.     

苯并呋喃／噻吩联二苯类PTP1B抑制剂三维构效关系研究

主要采用比较分子力场分析方法（CoMFA）对苯并呋喃／噻吩联二苯类PTP1B (protein tyrosine phosphatase 1B)抑制剂进行了三维构效关系的研究，考察了 静电场、立体场和氢键场对构效关系的影响，交叉系数q^2的值达到0．58，表明 CoMFA得到的构效关系模型比较理想，同时test set中分子的预测活性也表明，模 型具有较好的预测能力，研究还表明，氢键场的加入不一定有利于模型的改善，通 过对分子场等值面图的分析，可以观察到叠合分子周围立体场和静电场对化合物活 性的影响，为改进原有化合物的结构，提高它们的活性提供了指导，还尝试采用比 较分子相似性指数分析方法（CoMFA）对这一系列化合物作了研究，结果表明虽然 CoMFA中加入了疏水场，但是对于研究的体系，CoMFA的模型质量并没有显著提高。     

3D-QSAR Study on a Series of Indolo[1,2-b]quinazoline Derivatives with Anticancer Activity and their Molecular Design

Indolo[1,2-b]quinazoline derivatives have recently been reported as a type of compound with potential anticancer activity. On the basis of our the published two-dimensional quantitative structure-activity relationship (2D-QSAR) of these compounds, a further study on the three-dimensional quantitative structure-activity relationship (3D-QSAR) was carried out using the method of comparative molecular field analysis (CoMFA). A reasonable, receivable, and an effective 3D-QSAR model has been established, in which the correlation coefficient (r²) and cross-validation coefficient (q²) values are 0.986 and 0.695, respectively, the statistical squared deviation ratio (F) is 114.6, and the standard deviation (SD) is 0.084. The results suggest that the electrostatic effect of substituent R₁ and steric effect of substituent R₂ play a very important role in the improvement of the anticancer activity of these compounds. In this article, some significant conclusions, which are in good agreement with the conclusions obtained using 2D-QSAR, were drawn as follows. (1) The electrostatic effect in the substituent R₁ part plays a major role, and it is very important to make the first atom of R₁ carrying more positive charges in order to improve the anticancer activity of the compounds. (2) The steric effect plays a major role in the substituent R₂ part, and the volume of R₂ should be moderate. Based on the above conclusions, three new molecules of Indolo[1,2-b]quinazoline derivatives with higher anticancer activity have been theoretically designed and are waiting for support from experiment. The QSAR results can offer a theoretical reference for the pharmaceutical synthesis.     

3D-QSAR and molecular docking studies of 4-methyl quinazoline derivatives as PI3Kα inhibitors

A new series of 4- methyl quinazoline derivatives was synthesized and its anti-cancer activity was assessed. It was revealed that its compounds have potent inhibition on related phosphoinositide 3-kinases alpha (PI3Kα). In this study, the three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular docking approaches were performed on a series of 4-methyl quinazoline derivatives with PI3Kα inhibitors. The 3D-QSAR study was applied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods, which gave the cross-validation coefficient (Q²) values of 0.850 and 0.92, the determination coefficient (R²) values of 0.998 and 0.987, and the standard error of the estimate (SEE) values of 0.017 and 0.105, respectively. The acceptable values of determination coefficient (R² test) to CoMFA and CoMSIA respectively corresponding to values of 0.793 and 0.804 utilizing a test set of seven molecules prove the high predictive ability of this model. Using AutoDock tools, Molecular docking analysis was utilized to validate 3D-QSAR methods and to explain the binding site interactions and energy between the most active ligands and the PI3Kα (PDB ID: 4JPS) receptor. Based on these results, a novel series of 4- methyl quinazoline derivatives was predicted.     

2D and 3D‐QSAR studies on antiproliferative thiazolidine analogs

Two‐dimensional (2D) and three‐dimensional (3D) quantitative structure–activity relationships (QSARs) of 22 thiazolidine analogs with antiproliferative activity expressed as pIC₅₀, which is defined as the negative value of the logarithm of necessary molar concentration of these compounds to cause 50% growth inhibition against melanoma cell lines WM‐164, have been studied by using a combined method of the DFT, MM2 and statistics for 2D, as well as the comparative molecular field analysis (CoMFA) method for 3D. The established 2D‐QSAR model in training set comprised of random 18 compounds shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient (R = 0.832) and the square of the cross‐validation coefficient (q² = 0.803). The same model was further applied to predict pIC₅₀ values of the four compounds in the test set, and the resulting R reaching 0.784, further confirms that this 2D‐QSAR model has high predictive ability. The 3D‐QSAR model also shows good correlative and predictive capabilities in terms of R² (0.956) and q² (0.615) obtained from CoMFA model. Further, the robustness of the CoMFA model was verified by bootstrapping analysis (100 runs) with R (0.979) and SD_bs (0.056). It is very interesting to find that the results from 2D‐ and 3D‐QSAR analyses accord with each other, and they all show that the steric interaction plays a crucial role in determining the cytotoxicities of the compounds, and that selecting a moderate‐size or appropriate‐hydrophobicity substituent R as well as increasing the negative charges of C₄ on phenyl ring at the same time are advantageous to improving the cytotoxicity. Such results can offer some useful theoretical references for directing the molecular design and understanding the action mechanism of this kind of compound with antiproliferative activity. © 2008 Wiley Periodicals, Inc. Int J Quantum Chem, 2008