Field Analysis Techniques Research Articles

QSAR study of a large set of 3-pyridyl ethers as ligands of the α4β2 nicotinic acetylcholine receptor

Extensive 3D-QSAR studies were performed on 158 diverse analogues of 3-pyridyl ethers, which are excellent ligands of α4β2 neuronal nicotinic acetylcholine receptor (NnAChR). Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques were used to relate the binding affinities with the ligand structures. Two QSAR models were obtained using CoMFA and CoMSIA techniques. The two QSAR models were proved to be statistically significant and have high predictive power. The best CoMFA model yielded the cross-validated q 2 = 0.605 and the non-cross-validated r 2 = 0.862. The derived model indicated the importance of steric (85.9%) as well as electrostatic (14.1%) contributions. The CoMFA model demonstrated the steric field as the major descriptor of the ligand binding. The best CoMSIA model gave q 2 = 0.723 and r 2 = 0.685. This model showed that steric (30.3%) and H-bond interaction (61.8%) properties played major roles in ligand binding process. The squares of correlation coefficient for external test set of 28 molecules were 0.723 and 0.685 for the CoMFA model and the CoMSIA model, respectively. The two models were further graphically interpreted in terms of field contribution maps. SAR studies were also performed on different series of compounds in order to get a more reasonable understanding of the interactions between the ligands and the receptor. With the results, we have also presumed some assistant elements as supplements to the traditional pharmacophoric elements. A crude vision of ligand localization in the ligand-binding pocket of the receptor was also obtained, which would favor for the docking study of this kind of ligands.

QSAR Analyses of 3‐(4‐Benzylpiperidin‐1‐yl)‐N‐phenylpropylamine Derivatives as Potent CCR5 Antagonists.

CCR5 receptor binding affinity of a series of 3-(4-benzylpiperidin-1-yl)propylamine congeners was subjected to QSAR study using the linear free energy related (LFER) model of Hansch. Appropriate indicator variables encoding different group contributions and different physicochemical variables such as hydrophobicity (π), electronic (Hammett σ), and steric (molar refractivity, STERIMOL values) parameters of phenyl ring substituents of the compounds were used as predictor variables. The Hansch analysis explores the importance of the lipophilicity and electron-donating substituents for the binding affinity. However, this method could not give more insight into the structure−activity relationships because of the diverse molecular features in the data set. 3D-QSAR analyses of the same data set using Molecular Shape Analysis (MSA), Receptor Surface Analysis (RSA), and Molecular Field Analysis (MFA) techniques were also performed. The best model with acceptable statistical quality was derived from the MSA, which ...

3D-QSAR studies on tripeptide aldehyde inhibitors of proteasome using CoMFA and CoMSIA methods

The ubiquitin–proteasome pathway plays a crucial role in the regulation of many physiological processes and in the development of a number of major human diseases, such as cancer, Alzheimer’s, Parkinson’s, diabetes, etc. As a new target, the study on the proteasome inhibitors has received much attention recently. Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies using comparative molecule field analysis (CoMFA) and comparative molecule similarity indices analysis (CoMSIA) techniques were applied to analyze the binding affinity of a set of tripeptide aldehyde inhibitors of 20S proteasome. The optimal CoMFA and CoMSIA models obtained for the training set were all statistically significant with cross-validated coefficients ( q 2) of 0.615, 0.591 and conventional coefficients ( r 2) of 0.901, 0.894, respectively. These models were validated by a test set of eight molecules that were not included in the training set. The predicted correlation coefficients ( r 2) of CoMFA and CoMSIA are 0.944 and 0.861, respectively. The CoMFA and CoMSIA field contour maps agree well with the structural characteristics of the binding pocket of β5 subunit of 20S proteasome, which suggests that the 3D-QSAR models built in this paper can be used to guide the development of novel inhibitors of 20S proteasome.

Early Detection of Glaucomatous Visual Field Loss: Why, What, Where, and How

This article provides a basis for understanding modern visual field analysis techniques. It describes some of these tests, including their advantages and disadvantages, and the settings in which different tests might be used most successfully. It also includes an examination of the value of visual field assessment from a public health standpoint with particular attention paid to morbidity, cost/benefit ratios, and quality of life correlations.

QSAR Analyses of 3-(4-Benzylpiperidin-1-yl)-N-phenylpropylamine Derivatives as Potent CCR5 Antagonists

CCR5 receptor binding affinity of a series of 3-(4-benzylpiperidin-1-yl)propylamine congeners was subjected to QSAR study using the linear free energy related (LFER) model of Hansch. Appropriate indicator variables encoding different group contributions and different physicochemical variables such as hydrophobicity (pi), electronic (Hammett sigma), and steric (molar refractivity, STERIMOL values) parameters of phenyl ring substituents of the compounds were used as predictor variables. The Hansch analysis explores the importance of the lipophilicity and electron-donating substituents for the binding affinity. However, this method could not give more insight into the structure-activity relationships because of the diverse molecular features in the data set. 3D-QSAR analyses of the same data set using Molecular Shape Analysis (MSA), Receptor Surface Analysis (RSA), and Molecular Field Analysis (MFA) techniques were also performed. The best model with acceptable statistical quality was derived from the MSA, which showed the importance of the relative negative charge (RNCG): substituents with a high RNCG value have more binding affinity than the unsubstituted piperidine and phenyl (R1 position) congeners. The relative negative charge surface area (RNCS) is detrimental (e.g. R2 = 3,4-Cl2) for the activity. An increase in the length of the molecule in the Z dimension (Lz) is conducive (e.g. R3 = sulfonylmorpholino), while an increase in the area of the molecular shadow in the XZ plane (Sxz) is detrimental (e.g. R1 = N-c-hexylmethyl-5-oxopyrrolidin-3-yl) for the binding affinity. The presence of a chiral center makes the molecule less active (e.g. R1 = N-methyl-5-oxopyrrolidin-3-yl). An increase in the van der Waals area, the molecular volume, and the difference between the volume of the individual molecule and the shape reference compound are conducive (e.g. R3 = (CH3)2NSO2-) for the binding affinity. Substituents with higher JursFPSA_2 values (fractional charged partial surface area) like the N-methylsulfonylpiperidin-4-yl (R1 position) group have better binding affinity than the substituents such as 4-chlorophenylamino (R1 position). Unsubstituted piperidines (R1 position) with less JursFNSA_1 values have lower binding affinity than the 4-chlorophenyl substituted compounds. The MFA derived equation shows interaction energies at different grid points, while the RSA model shows the importance of hydrophobicity and charge at different regions of the molecules. The models were validated through the leave-one-out, leave-15%-out, and leave-25%-out cross-validation techniques. The developed models were also subjected to a randomization test (99% confidence level). Although the MSA derived models had excellent statistical qualities both for the training as well as test sets, RSA and MFA results for the test sets are not comparable statistically with the MSA derived models.

Exploring 3D-QSAR of thiazole and thiadiazole derivatives as potent and selective human adenosine A3 receptor antagonists+

Binding affinity data [Bioorg Med Chem (2004) 12:613-623] of thiazole and thiadiazole derivatives (n = 30) for the human adenosine A3 receptor subtype have been subjected to 3D-QSAR (Quantitative structure-activity relationships) analyses by molecular shape analysis (MSA) and molecular field analysis (MFA) techniques using Cerius2 Version 4.8. In the case of the MSA, the major steps were (1) generation of conformers and energy minimization; (2) hypothesizing an active conformer (global minimum of the most active compound); (3) selecting a candidate shape-reference compound (based on the active conformation); (4) performing pairwise molecular superimposition using the maximum common subgroup (MCSG) method; (5) measuring molecular shape commonality using MSA descriptors; (6) determining other molecular features by calculating spatial, electronic and conformational parameters; (7) selection of conformers; (8) generation of QSAR equations by genetic function algorithm (GFA) or stepwise regression. The best 3D-QSAR equation (MSA) obtained from GFA technique shows 70.0% predicted variance (leave-one-out) and 77.7% explained variance. This equation shows the importance of Jurs descriptors (atomic charge weighted positive surface area, relative negative charge and relative positive charge surface area), partial moment of inertia, energy of the most stable conformer and the ratio of common overlap steric volume to volume of individual molecules. In the case of stepwise regression, the best relation showed 46.1% predicted variance and 72.3% explained variance. In the case of MFA, the major steps were (1) generating conformers and energy minimization; (2) matching atoms using a maximum common substructure (MCS) search and aligning molecules using the default options; (3) setting MFA preferences (rectangular grid with 2 A step size, charges by the Gasteiger algorithm, H+ and CH3 as probes); (4) creating the field; (5) analysis by the Genetic partial least squares (G/PLS) method. The equation obtained was of excellent statistical quality: 96.1% explained variance and 71.6% predicted variance. Statistically reliable 3D-QSAR models obtained from this study suggest that these techniques could be useful to design potent A3 receptor antagonists.

Binding Model Construction of Antifungal 2-Aryl-4-chromanones Using CoMFA, CoMSIA, and QSAR Analyses

Flavonoids, generated by plants upon attack by a range of pathogens, are demonstrated to have a role in biotic and abiotic stress response phenomena in plants, and there is increasing evidence for the antibacterial, antifungal, and antiviral activities of these compounds. Using the bioisosterism strategy, a series of 2-aryl-4-chromanone derivatives based upon the structure of flavanones, a kind of flavonoid phytoalexins, were synthesized and tested for the antifungal activity against Pyricularia grisea, which have been reported in our previous papers. To further explore the comprehensive structure-activity relationship and construct the binding model for the antifungal compounds, two kinds of molecular field analysis techniques, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), were performed following a Hansch-Fujita QSAR study. Superimpositions were performed using three alignment rules, that is, centroid-based alignment, common substructure-based alignment, and field fit alignment, and statistically reliable models with good predictive power (CoMFA r(2) = 0.952, q(2) = 0.727; CoMSIA r(2) = 0.965, q(2) = 0.751) were achieved on the basis of the common substructure-based alignment. The combined results of CoMFA, CoMSIA, and former Hansch-Fujita QSAR analyses resulted in comprehensive understanding about the structure-activity relationships, which led to this construction of a plausible binding model of the title compounds.

Comparative molecular field analysis and comparative molecular similarity indices analysis of human thymidine kinase 1 substrates

Thymidine kinase 1 (TK1) is a key target for antiviral and anticancer chemotherapy. Three-dimensional quantitative structure–activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques was applied to analyze the phosphorylation capacity of a series of 31 TK1 substrates. The optimal predictive CoMFA model with 26 molecules provided the following values: cross-validated r 2 ( q 2) = 0.651, non-cross-validated r 2 = 0.980, standard error of estimate ( s) = 0.207, F = 129.3. For the optimal CoMSIA model the following values were found: q 2 = 0.619, r 2 = 0.994, s = 0.104, F = 372.2. The CoMSIA model includes steric, electrostatic, and hydrogen bond donor fields. The predictive capacity of both models was successfully validated by calculating known phosphorylation rates of five TK1 substrates that were not included in the training set. Contour maps obtained from CoMFA and CoMSIA models correlated with the experimentally developed SAR.

Improved detection of low vapor pressure compounds in air by serial combination of single-sided membrane introduction with fiber introduction mass spectrometry (SS-MIMS-FIMS)

The use of two methods in tandem, single-sided membrane introduction mass spectrometry (SS-MIMS) and fiber introduction mass spectrometry (FIMS), is presented as a technique for field analysis. The combined SS-MIMS-FIMS technique was employed in both a modified commercial mass spectrometer and a miniature mass spectrometer for the selective preconcentration of the explosive simulant o-nitrotoluene (ONT) and the chemical warfare agent simulant, methyl salicylate (MeS), in air. A home-built FIMS inlet was fabricated to allow introduction of the solid-phase microextraction (SPME) fiber into the mass spectrometer chamber and subsequent desorption of the trapped compounds using resistive heating. The SS-MIMS preconcentration system was also home-built from commercial vacuum parts. Optimization experiments were done separately for each preconcentration system to achieve the best extraction conditions prior to use of the two techniques in combination. Improved limits of detection, in the low ppb range, were observed for the combination compared to FIMS alone, using several SS-MIMS preconcentration cycles. The SS-MIMS-FIMS response for both instruments was found to be linear over the range 50 to 800 ppb. Other parameters studied were absorption time profiles, effects of sample flow rate, desorption temperature, fiber background, memory effects, and membrane fatigue. This simple, sensitive, accurate, robust, selective, and rapid sample preconcentration and introduction technique shows promise for field analysis of low vapor pressure compounds, where analyte concentrations will be extremely low and the compounds are difficult to extract from a matrix like air.

磁石内蔵誘導電動機の二次元磁場解析手法

磁石内蔵誘導電動機の二次元磁場解析手法

Three-dimensional constrained optimization of modular toroid-type SMES using co-evolutionary algorithm

In this paper, a modular toroid-type SMES was optimized using a recently developed constrained optimization technique named co-evolutionary augmented Lagrangian method (CEALM). The objective of the optimization was to minimize the total length of HTS superconductor satisfying some equality and inequality constraints. The constraints were calculated using 3-D magnetic field analysis techniques and an automatic tetrahedral mesh generator. Optimized results were verified by 3D finite element method (FEM).

PM誘導発電機の磁界解析

The permanent-magnet induction generator (PMIG) is a new type of induction machine that has a permanent-magnet rotor inside a squirrel-cage rotor. In this paper, a new technique for the magnetic field analysis of the PMIG is proposed. The proposed technique is based on the PMIG's equivalent circuit and the two-dimensional finite-element analysis (2D-FEA). To execute the 2D-FEA, the phasors of primary and secondary currents are calculated from the equivalent circuit, and the input data for the 2D-FEA is found by converting these phasors into the space vectors. As a result, the internal magnetic fields of the PMIG can be easily analyzed without complicated calculations.

Focusing leadership through force field analysis: new variations on a venerable planning tool

This forwards a new version of a tool long used in planned change and organizational development efforts – force field analysis. Existing applications of this technique are critiqued in light of cognitive heuristics known to erode judgment and analytical performance in plan development. A cognitive prompting template is combined with the existing the force field analysis technique to mitigate these problems. As such, the revised technique represents a significant improvement over the traditional application of the force field tool as used by the OD practitioner. Following an overview of the theoretical underpinnings of the revised technique, a case example is offered to illustrate the technique as it was used in a real organization. Finally, practical facilitation guidelines are offered to help leaders and planners conduct force field analysis sessions in multi‐stakeholder change efforts.

Mapping the Binding Site of a Large Set of Quinazoline Type EGF‐R Inhibitors Using Molecular Field Analyses and Molecular Docking Studies.

In the current work, three-dimensional QSAR studies for one large set of quinazoline type epidermal growth factor receptor (EGF-R) inhibitors were conducted using two types of molecular field analysis techniques: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). These compounds belonging to six different structural classes were randomly divided into a training set of 122 compounds and a test set of 13 compounds. The statistical results showed that the 3D-QSAR models derived from CoMFA were superior to those generated from CoMSIA. The most optimal CoMFA model after region focusing bears significant cross-validated r2cv of 0.60 and conventional r2 of 0.92. The predictive power of the best CoMFA model was further validated by the accurate estimation to these compounds in the external test set, and the mean agreement of experimental and predicted log(IC50) values of the inhibitors is 0.6 log unit. Separate CoMFA models were conducted to evaluate the ...

3-D Magnetic Field Analysis of Permanent Magnet Motor Considering Magnetizing, Demagnetizing and Eddy Current Loss

Rare-earth magnets, which have high energy product, have been widely used in several industrial applications such as voice coil motors for hard disk drives, MRI for medical devices and motors for electric vehicle. In order to realize a small and high performance device, the magnetic field analysis techniques are required. In this paper, we applied the magnetic field analysis to design the permanent magnet synchronous motors into the rail traction system. In the inverter fed motor drive, the eddy current loss in the permanent magnet increased. We simulated the effect that eddy current was decreased by using a divided permanent magnet. Furthermore, the permanent magnet tends to be demagnetized due to the effect of a demagnetizing field formed at high temperatures. However, according to our analysis, demagnetization does not occur within the range of our design specifications. Also, we performed magnetic field analysis assuming a pulse-type magnetization process and designed an optimal magnetizing coil.

Three-dimensional Structure–Activity Relationship Modeling of Cocaine Binding to Two Monoclonal Antibodies by Comparative Molecular Field Analysis

Successful immunotherapy of cocaine addiction and overdoses requires cocaine-binding antibodies with specific properties, such as high affinity and selectivity for cocaine. We have determined the affinities of two cocaine-binding murine monoclonal antibodies (mAb: clones 3P1A6 and MM0240PA) for cocaine and its metabolites by [3H]-radioligand binding assays. mAb 3P1A6 (Kd=0.22nM) displayed a 50-fold higher affinity for cocaine than mAb MM0240PA (Kd=11nM) and also had a greater specificity for cocaine. For the systematic exploration of both antibodies' binding specificities, we used a set of approximately 35 cocaine analogues as structural probes by determining their relative binding affinities (RBAs) using an enzyme-linked immunosorbent competition assay. Three-dimensional quantitative structure–activity relationship (3D-QSAR) models on the basis of comparative molecular field analysis (CoMFA) techniques correlated the binding data with structural features of the ligands. The analysis indicated that despite the mAbs' differing specificities for cocaine, the relative contributions of the steric (∼80%) and electrostatic (∼20%) field interactions to ligand-binding were similar. Generated three-dimensional CoMFA contour plots then located the specific regions about cocaine where the ligand/receptor interactions occurred. While the overall binding patterns of the two mAbs had many features in common, distinct differences were observed about the phenyl ring and the methylester group of cocaine. Furthermore, using previously published data, a 3D-QSAR model was developed for cocaine binding to the dopamine reuptake transporter (DAT) that was compared to the mAb models. Although the relative steric and electrostatic field contributions were similar to those of the mAbs, the DAT cocaine-binding site showed a preference for negatively charged ligands. Besides establishing molecular level insight into the interactions that govern cocaine binding specificity by biopolymers, the three-dimensional images obtained reflect the properties of the mAbs binding pockets and provide the initial information needed for the possible design of novel antibodies with properties optimized for immunotherapy.

Mapping the binding site of a large set of quinazoline type EGF-R inhibitors using molecular field analyses and molecular docking studies.

In the current work, three-dimensional QSAR studies for one large set of quinazoline type epidermal growth factor receptor (EGF-R) inhibitors were conducted using two types of molecular field analysis techniques: comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). These compounds belonging to six different structural classes were randomly divided into a training set of 122 compounds and a test set of 13 compounds. The statistical results showed that the 3D-QSAR models derived from CoMFA were superior to those generated from CoMSIA. The most optimal CoMFA model after region focusing bears significant cross-validated r(2)(cv) of 0.60 and conventional r(2) of 0.92. The predictive power of the best CoMFA model was further validated by the accurate estimation to these compounds in the external test set, and the mean agreement of experimental and predicted log(IC(50)) values of the inhibitors is 0.6 log unit. Separate CoMFA models were conducted to evaluate the influence of different partial charges (Gasteiger-Marsili, Gasteiger-Hückel, MMFF94, ESP-AM1, and MPA-AM1) on the statistical quality of the models. The resulting CoMFA field map provides information on the geometry of the binding site cavity and the relative weights of various properties in different site pockets for each of the substrates considered. Moreover, in the current work, we applied MD simulations combined with MM/PBSA (Molecular mechanics/Possion-Boltzmann Surface Area) to determine the correct binding mode of the best inhibitor for which no ligand-protein crystal structure was present. To proceed, we define the following procedure: three hundred picosecond molecular dynamics simulations were first performed for the four binding modes suggested by DOCK 4.0 and manual docking, and then MM/PBSA was carried out for the collected snapshots. The most favorable binding mode identified by MM/PBSA has a binding free energy about 10 kcal/mol more favorable than the second best one. The most favorable binding mode identified by MM/PBSA can give satisfactory explanation of the SAR data of the studied molecules and is in good agreement with the contour maps of CoMFA. The most favorable binding mode suggests that with the quinazoline-based inhibitor, the N3 atom is hydrogen-bonded to a water molecule which, in turn, interacts with Thr 766, not Thr 830 as proposed by Wissner et al. (J. Med. Chem. 2000, 43, 3244). The predicted complex structure of quinazoline type inhibitor with EGF-R as well as the pharmacophore mapping from CoMFA can interpret the structure activities of the inhibitors well and afford us important information for structure-based drug design.

About input data selection for the FEM analysis of bulk forming

Bulk forming processes are widely used in both the primary and secondary metal working industry, especially for discrete parts production. In that field quite complicated final shapes are often required but the process planning and tool design are still based mainly on empirical rules and gained experience. In order to assist these substantial engineering activities, some computer simulation packages have been recently developed. Most current numerical investigations are based on the finite element method rather than on the former slip-line field analysis or upper-bound elemental technique. All the numerical solutions, however, should be confirmed by model material experiments or, at least, compared with another numerical analysis. Accordingly, the purpose of this paper is to indicate the influence of input data selection on the results obtained by finite element simulation of bulk forming processes. The values of the friction factor and the metal flow stress have been varied independently using the FORM-2D software code for computer simulation.

2-Methoxyestradiol and analogs as novel antiproliferative agents: analysis of three-dimensional quantitative structure-activity relationships for DNA synthesis inhibition and estrogen receptor binding.

2-Methoxyestradiol (2-MEO), a metabolite of estrogen, is an attractive lead compound for the development of novel antitumor and anti-inflammatory agents, because it embodies antiproliferative and antiangiogenic activities in one molecule. However, the affinity of 2-MEO for the estrogen receptor would lead to undesirable side effects. As a prelude to the design of 2-MEO-like compounds with an optimal activity profile, we assayed 2-MEO and a series of analogs for their ability to cause G(1) cell-cycle arrest (by measuring inhibition of DNA synthesis in human cultured airway smooth muscle) and to inhibit binding of [(3)H]estradiol at the estrogen receptor (ER; from rat uterine smooth muscle). One compound, a diacetoxy enediol derivative, was identified with reasonable potency for DNA synthesis (pIC(50) = 5.97) but showed negligible affinity for the ER (pIC(50) < 5). Three-dimensional quantitative structure-activity relationships were developed for these activities using comparative molecular field analysis (CoMFA) techniques. Comparison of optimized CoMFA models revealed distinct structural requirements for DNA synthesis inhibition and ER binding. For example, DNA synthesis inhibition is enhanced by electropositive substitutions in the 2-position below the plane of the steroid A-ring, whereas ER binding is favored by electronegative substitution in this position. Similarly, DNA synthesis inhibition correlates negatively with increased steric bulk in regions clustered around the A and B rings; changes in steric bulk in these regions has little correlation with ER binding. These observations will guide the design of new analogs with improved potency for desired characteristics (e.g., DNA synthesis inhibition) with minimal unwanted activities (e.g., ER binding).

Determination of the quadrupole moments of four coupled quantum dots

A 3D numerical field analysis technique for the computation of multipole moments of molecules and artificial molecules has been developed. In this paper the application of this technique is illustrated by an example. The electrons are confined into quantum dots of an artificial molecule by a repelling electrostatic field applied to the surface of a semiconductor heterojunction structure. In the four dots two electrons are confined. In this case the molecule is neutral, its dipole moment is zero, thus its electrostatic field can be approximated by its quadrupole moment. The new technique is applied to compute the quadrupole moment. Copyright © 2001 John Wiley & Sons, Ltd.