FLUFF-BALL, a template-based grid-independent superposition and QSAR technique: validation using a benchmark steroid data set

The Flexible Ligand Unified Force Field (FLUFF) is a molecular mechanistic superposition algorithm utilizing a template structure, on top of which the ligand(s) are superimposed. FLUFF enables a flexible semiautomatic superimposition in which the ligand and the template are allowed to seek the best common conformation, which can then be used to predict the biological activity by Boundless Adaptive Localized Ligand (BALL). In BALL, the similarity of the electrostatic and van der Waals volumes of the template and ligand is evaluated using the template-based coordinate system which makes the FLUFF-BALL invariant as to the rotations and translations of the global coordinate system. When tested using the CBG (corticosteroid binding globulin) affinities of 31 benchmark steroids, the FLUFF-BALL technique produced results comparable to standard 3D-QSAR methods. Supplementary test calculations were performed with five additional data sets. Due to its high level of automation and high throughput, the FLUFF-BALL is highly suitable for use in drug design and in scanning of large molecular libraries.     

Probability issues in molecular design: predictive and modeling ability in 3D-QSAR schemes

In the current work we investigated 3D-QSAR data by the use of the coupled leave-several-out (LSO) and leave-one-out (LOO) cross-validation (CV) procedures. We verified the above mentioned scheme using both simulated data and real 3D QSAR data describing a series of CoMFA steroids, heterocyclic azo dyes and styrylquinoline HIV integrase inhibitors. Unlike in standard analyses, this technique characterizes individual method not by a single performance metrics but screens a whole possible modeling space by sampling different molecules into the training and test sets, respectively. This allowed us for the discussion of the information included in the estimators validating cross-validation procedures, as well as the comparison of the efficiency of several 3D QSAR schemes, in particular, Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Surface Analysis (CoMSA). Moreover, it allows one to acquire some general knowledge about predictive and modeling ability in 3D QSAR method.     

Comparative molecular field analysis of non-steroidal aromatase inhibitors related to fadrozole

Summary A series of non-steroidal inhibitors of aromatase, structurally related to fadrozole (2), was investigated with the aim of developing a 3D QSAR model using the Comparative Molecular Field Analysis (CoMFA) technique. The alignment of the molecules was performed following two approaches (atom-by-atom and field fit), both starting from an initial hypothesis of superimposition of fadrozole to a steroidal inhibitor (3). From a number of CoMFA models built with different characteristics, one was recognized as the most statistically relevant; this one is discussed in detail. The features of the 3D QSAR model are consistent with those of other 3D and QSAR models of aromatase and its inhibitors.     

Three-dimensional quantitative structure activity relationship (QSAR) of cytotoxic active 3,5-diaryl-4,5-dihydropyrazole analogs: a comparative molecular field analysis (CoMFA) revisited study

ABSTRACT: In vitro antitumor evaluation of the synthesized 46 compounds of 3,5-diaryl-4,5-dihydropyrazoles against EAC cell lines and 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA) methods were described. CoMFA derived QSAR model shows a good conventional squared correlation coefficient r2 and cross validated correlation coefficient r2 cv 0.896 and 0.568 respectively. In this analysis steric and electrostatic field contribute to the QSAR equation by 70% and 30% respectively, suggesting that variation in biological activity of the compounds is dominated by differences in steric (van der Waals) interactions. To visualize the CoMFA steric and electrostatic field from partial least squares (PLS) analysis, contour maps are plotted as percentage contribution to the QSAR equation and are associated with the differences in biological activity. BACKGROUND: Pyrazole derivatives exhibit a wide range of biological properties including promising antitumor activity. Furthermore, Aldol condensation assisted organic synthesis has delivered rapid routes to N-containing heterocycles, including pyrazoles. Combining these features, the use of chalconisation-assisted processes will provide rapid access to a targeted dihydropyrazoles library bearing a hydrazino 3D QSAR study using pharmacophore and Comparative Molecular Field Analysis (CoMFA) methods were described for evaluation of antioxidant properties. RESULTS: Chalcones promoted 1 of the 2 steps in a rapid, convergent synthesis of a small library of hydrazinyl pyrazole derivatives, all of which exhibited significant antitumor activity against Ehrlich Ascites Carcinoma (EAC) human tumor cell line comparable to that of the natural anticancer doxorubicin, as a reference standard during this study. In order to understand the observed pharmacological properties, quantitative structure-activity relationship (3D QSAR) study was initiated. CONCLUSIONS: Chalcones heating provides a rapid and expedient route to a series of pyrazoles to investigate their chracterization scavenging properties. Given their favorable properties, in comparison with known anticancer, these pyrazole derivatives are promising leads for further development and optimization.     

3D QSAR Modeling and Molecular Docking Studies on a Series of Triazole Analogues as Antibacterial Agents

The 3D QSAR analysis using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques is performed on novel nalidixic acid based 1,2,4-triazole derivatives suggested earlier as antibacterial agents. The CoMFA and CoMSIA models employed for a training set of 28 compounds gives reliable values of Q² (0.53 and 0.52, respectively) and R² (0.79 and 0.85, respectively). The contour maps produced by the CoMFA and CoMSIA models are used to determine a three-dimensional quantitative structure-activity relationship. Based on the 3D QSAR contours new molecules with high predicted activities are designed. In addition, surflex-docking is performed to confirm the stability of predicted molecules in the receptor.     

光系统Ⅱ抑制剂: 顺式氰基丙烯酸酯化合物的 三维构效关系的电子结构研究

以距离比较法所获得的顺式氰基丙烯酸酯化合物的活性构象为模板,对39个该类化合物采用比较分子场方法进行了三维构效关系的研究。结果表明,所获得的药效团模型具有很好的预测能力。同时采用量子化学的方法对活性构象模板分子电子结构作了讨论。     

嘧啶（氧）苯甲酸类除草剂的3D—QSAR研究

利用比较分子场分析 (Co MFA)方法 ,对 2 0种嘧啶 (氧 )苯甲酸类化合物进行了三维定量构效关系(3 D-QSAR)研究。得到了具有较强预测能力的 QSAR模型。并对此模型进行了验证 ,在此基础上 ,设计了具有更高活性的化合物。     

Partial least squares modeling and genetic algorithm optimization in quantitative structure-activity relationships

Quantitative structure-activity relationship (QSAR) studies based on chemometric techniques are reviewed. Partial least squares (PLS) is introduced as a novel robust method to replace classical methods such as multiple linear regression (MLR). Advantages of PLS compared to MLR are illustrated with typical applications. Genetic algorithm (GA) is a novel optimization technique which can be used as a search engine in variable selection. A novel hybrid approach comprising GA and PLS for variable selection developed in our group (GAPLS) is described. The more advanced method for comparative molecular field analysis (CoMFA) modeling called GA-based region selection (GARGS) is described as well. Applications of GAPLS and GARGS to QSAR and 3D-QSAR problems are shown with some representative examples. GA can be hybridized with nonlinear modeling methods such as artificial neural networks (ANN) for providing useful tools in chemometric and QSAR.     

2(1H)-喹啉-2,4-二酮类化合物抗小麦锈病的3D-QSAR研究

用比较分子力场分析(CoMFA)方法和比较分子相似性指数分析(CoMSIA)方法研究了21个2(1H)-喹啉-2,4-二酮类化合物抗小麦锈病的三维定量构效关系(3D-QSAR),发现用CoMFA方法可以找到最佳的3D-QSAR模型,并通过量子化学从头计算的方法研究了不同活性化合物的前线轨道及静电势分布图的差异.所得构效关系模型为发现更高活性的化合物提供理论指导.     

Ordinal classification using Comparative Molecular Field Analysis

Comparative Molecular Field Analysis (CoMFA) is most widely used as one of the 3-dimensional QSAR (3D-QSAR) methods to identify the relationship between chemical structure and biological activity. Conventional CoMFA requires at least 3 orders of experimental data, such as IC50 and Ki, to obtain a good model, although practically there are many screening assays where biological activity is measured only by a rating scale. Hence, rating classification-oriented CoMFA coupled with ordinal logistic regression has been developed, and its predictive ability and 3D graphical analysis ability have been investigated. As a result, this novel CoMFA (Logistic CoMFA) has been found to be more robust than conventional CoMFAs in both predictive and 3D graphical analysis abilities. Furthermore, Logistic CoMFA is useful since it can provide the probability of each rank.     

An Enhanced Comparative Molecular Field Analysis Method Using Genetic Algorithm

Comparativemoleculartieldanalysismethod(CoMFA)hasbecomeoneofthemostpowertbltoolsforthree-dimensionalquantitativestructureactivityrelationshipstudies(3D-QSAR:)sinceitsadventin1988].Overthepastdecade.ithasbeenwidelyappliedincomputer-aideddrugdesign.CoMFAisusedmainlytoinvestigatestructure-activityrelationshipsandtoforecastthepotencyofnewanalogues.Moreover,italsohasutilityin3O-strLlcturesearchingandautomateddesignofnewligands,theinvestigationofthemechanismoforganicreactions,andmodelingof3D-s…     

1－环丙基－5，7，8取代－6－氟－1，4－二氢－4－氧－3－喹啉羧酸定量构效关系的比较分子力场分析研究

利用３Ｄ－ＱＳＡＲ中的比较分子力场分析法（ＣｏＭＦＡ）研究了标题化合物的定量构效关系。通过不同探针与空间网格点间隙的对比研究，发现以　、空间网格点间隙２０ｎｍ结果为最优。从　值来看，ＣｏＭＦＡ计算得到的模型有很高的预测性。依据ＣｏＭＦＡ系数图设计一些新化合物并预测了它们的活性，结果表明：对抑制革兰氏阳性菌有数种化合物的活性比迄今合成的化合物要高。     

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.     

Application of multivariate data analysis methods to Comparative Molecular Field Analysis (CoMFA) data: Proton affinities and pKa prediction for nucleic acids components

Multivariate data analysis methods (Principal Component Analysis (PCA) and Partial Least Squares (PLS)) are applied to the analysis of the CoMFA (Comparative Molecular Field Analysis) data for several nucleic acids components. The data set includes nitrogenated bases, nucleosides, linear nucleotides, 3, 5-cyclic nucleotides and oligonucleotides. PCA is applied to study the structure of the CoMFA data and to detect possible outliers in the data set. PLS is applied to correlate the CoMFA data with either calculated AM1 proton affinities or with experimental pKa values. The possibility of making a prediction of pKa values directly from 3D structures of the monomers for polynucleotides is also shown. The influence of the superposition criteria and of conformational changes along the glycosidic bond on the pKa prediction are studied as well.     

Sample-distance partial least squares: PLS optimized for many variables,with application to CoMFA

Summary Three-dimensional molecular modeling can provide an unlimited number m of structural properties. Comparative Molecular Field Analysis (CoMFA), for example, may calculate thousands of field values for each model structure. When m is large, partial least squares (PLS) is the statistical method of choice for fitting and predicting biological responses. Yet PLS is usually implemented in a property-based fashion which is optimal only for small m. We describe here a sample-based formulation of PLS which can be used to fit any single response (bioactivity). SAMPLS reduces all explanatory data to the pairwise distances among n sample (molecules), or equivalently to an n-by-n covariance matrix C. This matrix, unmodified, can be used to fit all PLS components. Furthermore, SAMPLS will validate the model by modern resampling techniques, at a cost independent of m. We have implemented SAMPLS as a Fortran program and have reproduced conventional and cross-validated PLS analyses of data from two published studies. Full (leaveach-out) cross-validation of a typical CoMFA takes 0.2 CPU s. SAMPLS is thus ideally suited to structure-activity analysis based on CoMFA fields or bonded topology. The sample-distance formulation also relates PLS to methods like cluster analysis and nonlinear mapping, and shows how drastically PLS simplifies the information in CoMFA fields.Abbreviations PLS partial least squares - SAMPLS sample-distance partial least squares - CoMFA comparative molecular field analysis.     

应用分子图形学、分子力学、量子化学及静电势研究农药分子结构与性能关系(ΧⅢ)──用比较分子力场方法研究4-肟醚基喹唑啉类化合物的结构与抗烟草花叶病毒活性的三维构效关系

应用比较分子力场分析 (Co MFA)方法研究 4 -肟醚基喹唑啉类化合物抗烟草花叶病毒活性的三维构效关系 (3 D-QSAR) ,引入分子的摩尔折射率 (MR)和偶极矩 (DIPOLE)分别作为 Co MFA的第三和第四个场 .在此基础上进行偏最小二乘 (PLS)分析 :交叉验证 (leave-one-out)结果为 r2cv=0 .4 43 ,非交叉验证 (novalidation)结果为 r2 =0 .93 2 ,说明所建立的模型有较好的可靠性 ,并且在三维等值线图的基础上得到了一个此类化合物的模拟作用模型 ,据此可生长出一系列先导分子     

Partial Least Squares Modeling and Genetic Algorithm Optimization in Quantitative Structure-Activity Relationships

Abstract

Quantitative structure-activity relationship (QSAR) studies based on chemometric techniques are reviewed. Partial least squares (PLS) is introduced as a novel robust method to replace classical methods such as multiple linear regression (MLR). Advantages of PLS compared to MLR are illustrated with typical applications. Genetic algorithm (GA) is a novel optimization technique which can be used as a search engine in variable selection. A novel hybrid approach comprising GA and PLS for variable selection developed in our group (GAPLS) is described. The more advanced method for comparative molecular field analysis (CoMFA) modeling called GA-based region selection (GARGS) is described as well. Applications of GAPLS and GARGS to QSAR and 3D-QSAR problems are shown with some representative examples. GA can be hybridized with nonlinear modeling methods such as artificial neural networks (ANN) for providing useful tools in chemometric and QSAR.     

咪唑啉酮类除草剂的三维构效关系研究

从三维角度出发,采取不同的构象搜索方法得到了咪唑酮类化合物分子的活性构象。利用比较分子场分析方法进一步证实了模板分子构象的正确性。并从静电场、立体场及活性关系等方面进行了三维定量构效关系研究,得到了具有较强预测能力的QSAR模型。