Comparative molecular field analysis (CoMFA) for phosphodiesterase (PDE) IV inhibitors

A comparative molecular field analysis (CoMFA) for phosphodiesterase (PDE) IV inhibitors has been performed to correlate their chemical structures with their observed biological activity. In this study, CoMFA model based on docking mode for active site of PDE IV can describe the relative change in magnitude of the steric and electrostatic fields as a function of the compounds. Pyridine N-oxide and pyridine group of each compound are aligned toward the metal ion in S2-sub pocket of PDE IV. The study provided a statistically valid model with good correlation and predictive power, and consequently we identified some key features that may be used to design new derivatives.     

Comparative molecular field analysis (CoMFA) of new herbicidal sulfonylurea compounds

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A comparison of progestin and androgen receptor binding using the CoMFA technique

Summary A series of 48 steroids has been studied with the SYBYL QSAR module using Relative Binding Affinities (RBAs) to progesterone and androgen receptors obtained from the literature. Models for the progesterone and androgen data were developed. Both models show regions where sterics and electrostatics correlate to binding affinity but are different for androgen and progesterone which suggests differences possibly important for receptor selectivity. The progesterone model is more predictive than the androgen (predictive r² of 0.725 vs. 0.545 for progesterone and androgen, respectively).     

Use of the hydrogen bond potential function in a comparative molecular field analysis (CoMFA) on a set of benzodiazepines

Summary The results of the GRID-Comparative Molecular Field Analysis (CoMFA) were compared with those of the SYBYL-CoMFA in a study of benzodiazepines. The results demonstrate that the hydrogen bonding function using the GRID H₂O probe in a CoMFA can successfully describe the hydrophobic effects of substituents without any bias or preconcept of their effects in the development.     

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.     

Prediction of LUMO energy and rate constant by comparative molecular field analysis (CoMFA)

We used the comparative molecular field analysis (CoMFA) method to correlate the rate constant (log k) for the S_N2 reaction of benzyl benzenesulfonates and p-methoxybenzylamines. Molecular fields calculated with a C⁺ probe produced a good correlation with a small standard deviation and a high correlation coefficient with cross validation. This study demonstrated that CoMFA is an excellent method in predicting the physicochemical properties of the molecule such as LUMO energy and rate constants. © 1995 by John Wiley & Sons, Inc.     

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) of thiazolone derivatives as hepatitis C virus NS5B polymerase allosteric inhibitors

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models for a series of thiazolone derivatives as novel inhibitors bound to the allosteric site of hepatitis C virus (HCV) NS5B polymerase were developed based on CoMFA and CoMSIA analyses. Two different conformations of the template molecule and the combinations of different CoMSIA field/fields were considered to build predictive CoMFA and CoMSIA models. The CoMFA and CoMSIA models with best predictive ability were obtained by the use of the template conformation from X-ray crystal structures. The best CoMFA and CoMSIA models gave q (2) values of 0.621 and 0.685, and r (2) values of 0.950 and 0.940, respectively for the 51 compounds in the training set. The predictive ability of the two models was also validated by using a test set of 16 compounds which gave r (pred) (2) values of 0.685 and 0.822, respectively. The information obtained from the CoMFA and CoMSIA 3D contour maps enables the interpretation of their structure-activity relationship and was also used to the design of several new inhibitors with improved activity.     

A comparative molecular field analysis (CoMFA) study using semiempirical, density functional, ab initio methods and pharmacophore derivation using DISCOtech on sigma 1 ligands

The Comparative Molecular Field Analysis (CoMFA) was developed to investigate a three-dimensional quantitative structure activity relationship (3D-QSAR) model of ligands for the sigma 1 receptor. The starting geometry of sigma-1 receptor ligands was obtained from the Tripos force field minimizations and conformations were decided from DISCOtech using the SYBYL 6.8. program. The structures of 48 molecules were fully optimized at the ab initio HF/3-21G* and semiempirical AM1 calculations using GAUSSIAN 98. The electrostatic charges were calculated using several methods such as semiempirical AM1, density functional B3LYP/3-21G*, and ab initio HF/3-21G*, MP2/3-21G* calculations within GAUSSIAN 98. Using the optimized geometries, the CoMFA results derived from the HF/3-21G method were better than those from AM1. The best CoMFA was obtained from HF/3-21G* optimized geometry and charges (R2 = 0.977). Using the optimized geometries, the CoMFA results derived from the HF/3-21G methods were better than those from AM1 calculations. The training set of 43 molecules gave higher R2 (0.989-0.977) from HF/3-21G* optimized geometries than R2 (0.966-0.911) values from AM1 optimized geometries. The test set of five molecules also suggested that HF/3-21G* optimized geometries produced good CoMFA models to predict bioactivity of sigma 1 receptor ligands but AM1 optimized geometries failed to predict reasonable bioactivity of sigma 1 receptor ligands using different calculations for atomic charges.     

Multilinear regression and comparative molecular field analysis (CoMFA) of azo dye-fiber affinities. 2. Inclusion of solution-phase molecular orbital descriptors

For a data set with 30 direct azo dyes taken from literature, quantitative structure-activity relationship (QSAR) analyses have been performed to model the affinity of the dye molecules for the cellulose fiber. The electronic structure of the compounds was characterized using quantum chemical gas-phase (AM1) and continuum-solvation molecular orbital parameters. As regards the solution phase, COSMO appears to be better suited than SM2 in quantifying relative trends of the aqueous solvation energy. For the dye-fiber affinity, the leave-one-out prediction capability of multilinear regression equations is superior to CoMFA, with predictive squared correlation coefficients ranging from 0.63 (pure CoMFA) to 0.89. At the same time, solution-phase CoMFA is superior to previously derived AM1-based CoMFA models. As a general trend, the dye-fiber affinity increases with increasing electron donor capacity that corresponds to an increasing hydrogen bond acceptor strength of the azo dyes. The discussion includes the consideration of structural features that are likely to be involved in dye-fiber and dye-dye hydrogen bonding interactions, and possible links between CoMFA electrostatic results and the atomic charge distribution of the compounds.     

Prediction of hydration free energies for the SAMPL4 diverse set of compounds using molecular dynamics simulations with the OPLS-AA force field

All-atom molecular dynamics computer simulations were used to blindly predict the hydration free energies of a range of small molecules as part of the SAMPL4 challenge. Compounds were parametrized on the basis of the OPLS-AA force field using three different protocols for deriving partial charges: (1) using existing OPLS-AA atom types and charges with minor adjustments of partial charges on equivalent connecting atoms and derivation of new parameters for a number of distinct chemical groups (N-alkyl imidazole, nitrate) that were not present in the published force field; (2) calculation of quantum mechanical charges via geometry optimization, followed by electrostatic potential (ESP) fitting, using Jaguar at the LMP2/cc-pVTZ(-F) level; and (3) via geometry optimization and CHelpG charges (Gaussian09 at the HF/6-31G* level), followed by two-stage RESP fitting. The absolute hydration free energy was computed by an established protocol including alchemical free energy perturbation with thermodynamic integration. The use of standard OPLS-AA charges (protocol 1) with a number of newly parametrized charges and the use of histidine derived parameters for imidazole yielded an overall root mean square deviation of the prediction from the experimental data of 1.75 kcal/mol. The precision of our results appears to be mainly limited by relatively poor reproducibility of the Lennard-Jones contribution towards the solvation free energy, for which we observed large variability that could be traced to a strong dependence on the initial system conditions.     

Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds

A novel method (in the context of quantitative structure-activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure-activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log P data from a widely used steroid benchmark data set.     

Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds

A novel method (in the context of quantitative structure–activity relationship (QSAR)) based on the k nearest neighbour (kNN) principle, has recently been introduced for the derivation of predictive structure–activity relationships. Its performance has been tested for estimating the estrogen binding affinity of a diverse set of 142 organic molecules. Highly predictive models have been obtained. Moreover, it has been demonstrated that consensus-type kNN QSAR models, derived from the arithmetic mean of individual QSAR models were statistically robust and provided more accurate predictions than the great majority of the individual QSAR models. Finally, the consensus QSAR method was tested with 3D QSAR and log?P data from a widely used steroid benchmark data set.     

Conformational analysis of synthetic neolignans active against leishmaniasis,using the molecular mechanics method (MM2)

Conformational analysis of 20 neolignans was performed to determine the most probable conformer that may fit the receptor. The molecular mechanics method (MM2) was employed to construct conformational maps in both a vacuum and a biological environment. Boltzmann's distribution among several local minima was calculated. © 1997 by John Wiley & Sons, Inc. J Comput Chem 18: 712–721, 1997     

Hydration of synthetic polydialkylphosphate (PPF) — a simplified model for natural teichoic acids

The hydration of synthetic poly(P-hydroxy 1,3 propylenephosphate) in hydrogen and mixed H⁺-Mg⁺² forms has been studied by means of the pulsed NMR method. This polymer may be regarded as a simplified model of natural teichoic acids appearing in a cell wall of gram-positive bacteria. The investigations of nuclear magnetic relaxation of water protons in polymer gels prove the existence of two phases of water. One is water strongly bound to phosphate groups which does not freeze at low temperature and the other is weakly bound water freezing at 243–253 K. The distribution of correlation times has been stated for water in the first hydration shell of phosphate groups. For both acid and magnesium salt forms the hydration shell is composed of three water molecules.