Chemical Feature-based Pharmacophore Research Articles

QSAR studies on 3-(4-biphenylmethyl) 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] Pyridine derivatives as angiotensin II (AT1) receptor antagonist

QSAR studies were performed for correlating the chemical composition of 3-(4-biphenylmethyl) 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] pyridines bearing aryl acetic acid esters and acetamides as angiotensin II AT(1) receptor antagonist. Four different quantitative structure-property relationship (QSAR) methods namely two-dimensional (2D-QSAR), group-based QSAR, k-nearest neighbor and Pharmacophore Modeling were employed to obtain statistically significant models. The statistically significant best 2D-QSAR model having correlation coefficient r(2) = 0:8940 and cross-validated squared correlation coefficient q(2) = 0:7648 with external predictive ability of pred_r(2) = 0:8177,pred_r(2)se = 0.4119 and best group-based QSAR model having r(2) = 0:7392 and q(2) = 0:6710with pred_r(2) = 0:7503was developed by SA-principal component regression. The most predictive k-nearest neighbor model derived from the superposition of conformations has good cross-validated q(2) = 0:7637 and satisfied predictive ability r(2)_pred = 0.7143. Continuing with compounds of substituted 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] pyridine derivatives chemical feature-based pharmacophore models with lowest RMSD value (0.3292 Å) consists of two Hac (Hydrogen bond acceptor), negative ionizable, and two AroC (Aromatic) features are important for the activity. The study suggested that substitution of group at R, R 1, R 2 and Ar, and position on 4, 5-dihydro-4-oxo-3H-imidazo [4, 5-c] pyridine ring with more electronegative nature and low bulkiness are favorable for the antihypertensive activity. These theoretical results may provide a useful reference for understanding the action mechanism and designing potential angiotensin II (AT1) receptor antagonist.

Structural requirements of N-aryl-oxazolidinone-5-carboxamide derivatives for anti-HIV protease activity using molecular modelling techniques

Two- and three-dimensional quantitative structure–activity relation (QSAR) models were generated for a series of N-aryl-oxazolidinone-5-carboxamide derivatives to identify potent anti-HIV-1 integrase ligands. The studies were performed by the partial least squares method coupled with various feature selection methods, such as genetic algorithm (GA) and stepwise methods, to derive QSAR models, which were further validated for statistical significance and predictive ability by internal and external validation. The best two-dimensional QSAR model was selected, which had a correlation coefficient, r2, of 0.9246, a cross-validated squared correlation coefficient, q2, of 0.7726 and an external predictive ability, pred_r2, of 0.8331. This model was developed by GA partial least squares with descriptors such as SsNH2 count, SssOE index, T_N_F_4, SsOH count and T_O_O_2. Three-dimensional QSAR studies gave reasonably good predictive models with a high cross-validated q2 value of 0.6953 and pred_r2 of 0.7499 in the GA k nearest neighbour molecular field analysis method. For a series of N-aryl-oxazolidinone-5-carboxamide derivatives, chemical feature-based pharmacophore models with the lowest root mean square deviation value (0.00765nm) showed one aromatic feature, two hydrogen bond acceptors, one hydrogen bond donor and one aliphatic feature. The information provided by two- and three-dimensional QSAR models may lead to better understanding of the structural requirements of oxazolidinone-5-carboxamides and help in designing novel, potent anti-HIV-1 integrase molecules.

Discovery of new lead pyrimidines derivatives as potential cannabinoid CB1 receptor antagonistic through molecular modeling and pharmacophore approach

A set of 58 compounds of pyrimidines derivatives was subjected to molecular modeling studies using two-dimensional, k-Nearest Neighbor (kNN), and pharmacophore methods. Two-dimensional-QSAR modeling using simulated annealing or stepwise coupled partial least squares (SA-PLS and SW-PLS) methods identified some important topological indices, hydrophobic thermodynamic electronic and spatial descriptors properties descriptors as important factor for cannabinoid CB1 activity. The best quantitative structure–activity relationship models were further validated by leave-one-out method of cross-validation. The best 2D-QSAR model was selected, having showed best predictability of cannabinoid CB1 receptor activity with cross-validated value (q2) = 0.7942, coefficient of determination (r2) = 0.9054, and r2_pred value of 0.8269 was developed by SA-PLS. kNN-MFA 3D QSAR was performed on the same series to correlate the effects of electrostatic, steric, and hydrophobic parameters with the cannabinoid CB1 activity using simulated annealing, stepwise forward backward regression and genetic algorithm methods. The best 3D model SA-PLS shows electrostatic descriptor present near R1 of the pyrimidine suggested that the electron-withdrawing group is required for enhancing the biological activity. The presence of steric descriptors with negative coefficients simultaneously at chloro and R2-positions of the pyrimidine ring, suggests the favorable of that less steric or less bulky substituents groups in these regions for producing potent cannabinoid CB1 receptor. Chemical feature-based pharmacophore models with lowest RMSD value (0.0681 A), consists of two AroC feature (aromatic), one HDr (hydrogen-bond donor), one HAc (hydrogen-bond acceptor), and one (aliphatic) features was developed. This work may provide a platform for generating leads for novel cannabinoid CB1 receptor inhibitors.

QSAR and pharmacophore approach on substituted imidazole derivatives as angiotensin II receptor antagonists

A QSAR analyses of 32 aminomethyl and acylaminomethyl substituents derivatives were carried out to interpret the relationship between structural properties and angiotensin II AT1 receptor activity. Two-dimensional (2D-QSAR), Group-based (G-QSAR), 3D-QSAR, and pharmacophore mapping studies were performed using partial least square and k-nearest neighbor methodology coupled with various feature selection methods, viz. stepwise, genetic algorithm, and simulated annealing (SA) to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The activity contributions of whole compounds and their substituents were determined from regression equation. The statistically significant best 2D-QSAR model 1 having r 2 = 0.8754 and q 2 = 0.7231 with $$ {\text{pred}}\_r^{ 2} = \, 0. 8 3 8 9 $$ was developed by GA-PLS with the descriptors like SdssCcount, SssNHcount and SaaaCcount. The best Group-based QSAR model-5 having r 2 = 0.7516 and q 2 = 0.6714 with $$ {\text{pred}}\_r^{ 2} = \, 0. 7 3 0 9 $$ was developed by GA-PLS. The 2D-QSAR results agreed with those from the Group based QSAR model and suggested the structural features of SssOE-index, Mol.Wt and SssNHE-index functionality that would enhance the antihypertensive activity of the imidazole derivatives in this series. The results of two-dimensional, Group-based QSAR showed that a combination of topological indices, hydrophobic properties, and auto-correlation descriptors of different atomic properties could be explored to design potent antihypertensive agents. The k-nearest neighbor approach was used to generate three-dimensional quantitative structure–activity relationship (3D-QSAR) models for these sets of molecules. The present study is an attempt in this direction seeking for the development and comparison of QSAR models of substituted imidazoles by different feature selection methods, which ultimately establishes the superiority of the GA-based models. Statistically genetic algorithm (GA) selection k-nearest neighbor (GA-kNN-MFA) model with respect to both the internal (q 2 = 0.7856) as well as external (pred_r 2 = 0.8193) model validation and correctly predicts the activity of calculated 78.56 % and calculated 81.93 % for the training and test set, respectively. Continuing with the series of substituted imidazoles derivatives, chemical feature-based pharmacophore models with lowest RMSD value (0.1397 A), consisting of one aromatic carbon center, two hydrogen bond acceptor, and one hydrogen bond donor features, was developed. The information rendered by 2D-QSAR, Group-based QSAR, k-nearest neighbor, and pharmacophore identification models may lead to a better understanding of structural requirements of antihypertensive agents and can help in the design of novel potent molecules.

Comparative QSAR and pharmacophore modeling of substituted 2-[2′-(dimethylamino) ethyl]-1, 2-dihydro-3H-dibenz[de,h]isoquinoline-1,3-diones derivatives as anti-tumor activity

To elucidate the structural properties required for anti-tumor activity, four different molecular modeling techniques; two-dimensional (2D-QSAR), group-based QSAR (G-QSAR), 3D-QSAR, and pharmacophore identification studies have been carried out on a series of substituted 2-[2′-(dimethylamino) ethyl]-1, 2-dihydro-3H-dibenz[de,h]isoquinoline-1,3-diones derivatives. The partial least square (PLS) regression method and k-nearest neighbor -MFA methodology coupled with feature selection methods viz. genetic algorithm and simulated annealing were applied to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. Further analysis using two-dimensional, group-based QSAR and k-nearest neighbor QSAR technique identifies a suitable model obtained by genetic algorithm and simulated annealing coupled with partial least square method leading to anti-tumor activity prediction. The statistically significant best 2D-QSAR model having r2 = 0.9041 and q2 = 0.8357 with pred_r2 = 0.8633 and best group-based QSAR model having r2 = 0.7315 and q2 = 0.6743 with pred_r2 = 0.7950 was developed by Genetic algorithm (melanoma)-PLS method. Further analysis using three-dimensional QSAR technique identifies a suitable model obtained by GA-partial least square method leading to anti-tumor activity prediction. The conformational alignment provided by the above-best pharmacophore was found to be highly robust as proved by the additional kNN-MFA approach studies. The best Genetic algorithm (melanoma)-PLS model with good external and internal predictivity for the training and test set has shown cross validation (q2) and external validation (pred_r2) values of 0.78 and 0.85, respectively. The influences of steric, electrostatic, and hydrophobic field effects generated by the contribution plot are analyzed and discussed. Continuing with the series of substituted 2-[2′-(dimethylamino) ethyl] -1, 2-dihydro-3H-dibenz[de,h]isoquinoline-1,3-diones derivatives chemical feature-based pharmacophore models with lowest RMSD value (0.0861 A°), consists of one AroC (aromatic), two HAc (hydrogen-bond acceptor), and one negative ionizable features were developed. In addition to the 3D QSAR models, impact of two-dimensional and group-based (2D; thermodynamic, structural, and topological including E-state parameters) descriptors toward the inhibitory activity was also studied. The information rendered by 2D-QSAR, group-based QSAR, k-nearest neighbor and Pharmacophore identification models may lead to a better understanding of structural requirements of anti-tumor and can help in the design of novel potent molecules.

Identification of novel inhibitors of HIV-1 integrase using pharmacophore-based virtual screening combined with molecular docking strategies

Human immunodeficiency virus type-1 (HIV-1) integrase is one of three enzymes encoded by the HIV genome for effective viral replication, and therefore an attractive target for chemotherapeutic interventions in the development of AIDS treatment. In this study, chemical feature-based pharmacophore models of different classes of integrase strand transfer inhibitors have been developed. The best HypoRefine pharmacophore models, Hypo1, which have the best correlation coefficients (0.92) and the lowest RMSs (0.78), contain two hydrogen bond acceptor lipids, one hydrogen bond donor, and one hydrophobic aromatic with four excluded volumes. After filtering by Lipinski’s rule of five, the best pharmacophore model was utilized as a 3D search query to perform a virtual screening to retrieve potential inhibitors. The hit compounds were subsequently subjected to docking studies by GOLD program to refine the retrieved hits. Finally, 4 top ranked compounds based on GOLD score fitness function and rescoring by X-score were investigated for compliance with the standard ranges through in silico ADME studies.

First chemical feature-based pharmacophore modeling of potent retinoidal retinoic acid metabolism blocking agents (RAMBAs): Identification of novel RAMBA scaffolds

The first three-dimensional (3D) pharmacophore model was developed for potent retinoidal retinoic acid metabolism blocking agents (RAMBAs) with IC 50 values ranging from 0.0009 to 5.84 nM. The seven common chemical features in these RAMBAs as deduced by the Catalyst/HipHop program include five hydrophobic groups (hydrophobes), and two hydrogen bond acceptors. Using the pharmacophore model as a 3D search query against NCI and Maybridge conformational Catalyst formatted databases; we retrieved several compounds with different structures (scaffolds) as hits. Twenty-one retrieved hits were tested for RAMBA activity at 100 nM concentration. The most potent of these compounds, NCI10308597 and HTS01914 showed inhibitory potencies less (54.7% and 53.2%, respectively, at 100 nM) than those of our best previously reported RAMBAs VN/12-1 and VN/14-1 (90% and 86%, respectively, at 100 nM). Docking studies using a CYP26A1 homology model revealed that our most potent RAMBAs showed similar binding to the one observed for a series of RAMBAs reported previously by others. Our data shows the potential of our pharmacophore model in identifying structurally diverse and potent RAMBAs. Further refinement of the model and searches of other robust databases is currently in progress with a view to identifying and optimizing new leads.

A Three‐Dimensional Pharmacophore Model for IMPDH Inhibitors

Inosine 5'-monophosphate dehydrogenase (IMPDH) is a key enzyme in the de novo synthesis of guanosine nucleotides. It is considered an important target in the quest for drugs in the immunosuppressive, antiviral, antibacterial and anticancer therapeutic areas. In this study, a chemical feature-based pharmacophore model of IMPDH inhibitors has been firstly developed with the aid of the HypoRefine protocol within Discovery Studio 2.5. The best model for IMPDH inhibitors, Hypo1-1, was characterized by the best correlation coefficient (0.97595) and the lowest RMSD (0.582058). It consisted of one hydrogen-bond donor, one hydrogen-bond acceptor, one aromatic ring and one hydrophobic feature, as well as two excluded volumes. The model was validated using a wide range of test molecules and a cross-validation. Furthermore, the pharmacophore features were confirmed by molecular docking studies. The pharmacophore model could quantitatively predict inhibitor activity and identify highly potent molecules. Therefore, the present results could be valuable for the discovery and development of specific IMPDH inhibitors.

Development of predictive pharmacophore model for in silico screening, and 3D QSAR CoMFA and CoMSIA studies for lead optimization, for designing of potent tumor necrosis factor alpha converting enzyme inhibitors

A chemical feature-based pharmacophore model was developed for Tumor Necrosis Factor-alpha converting enzyme (TACE) inhibitors. A five point pharmacophore model having two hydrogen bond acceptors (A), one hydrogen bond donor (D) and two aromatic rings (R) with discrete geometries as pharmacophoric features was developed. The pharmacophore model so generated was then utilized for in silico screening of a database. The pharmacophore model so developed was validated by using four compounds having proven TACE inhibitory activity which were grafted into the database. These compounds mapped well onto the five listed pharmacophoric features. This validated pharmacophore model was also used for alignment of molecules in CoMFA and CoMSIA analysis. The contour maps of the CoMFA/CoMSIA models were utilized to provide structural insight for activity improvement of potential novel TACE inhibitors. The pharmacophore model so developed could be used for in silico screening of any commercial/in house database for identification of TACE inhibiting lead compounds, and the leads so identified could be optimized using the developed CoMSIA model. The present work highlights the tremendous potential of the two mutually complementary ligand-based drug designing techniques (i.e. pharmacophore mapping and 3D-QSAR analysis) using TACE inhibitors as prototype biologically active molecules.

Structural basis for ligand recognition at the benzodiazepine binding site of GABA A α3 receptor, and pharmacophore-based virtual screening approach

Given the heterogeneity of GABA A receptor, the pharmacological significance of identifying subtype selective modulators is increasingly being recognized. Thus, drugs selective for GABA A α 3 receptors are expected to display fewer side effects than the drugs presently in clinical use. Hence we carried out 3D QSAR (three-dimensional quantitative structure–activity relationship) studies on a series of novel GABA A α 3 subtype selective modulators to gain more insight into subtype affinity. To identify the 3D functional attributes required for subtype selectivity, a chemical feature-based pharmacophore, primarily based on selective ligands representing diverse structural classes was generated. The obtained pseudo receptor model of the benzodiazepine binding site revealed a binding mode akin to “Message–Address” concept. Scaffold hopping was carried out across multi-conformational May Bridge database for the identification of novel chemotypes. Further a focused data reduction approach was employed to choose a subset of enriched compounds based on “Drug likeness” and “Similarity-based” methods. These results taken together could provide impetus for rational design and optimization of more selective and high affinity leads with a potential to have decreased adverse effects.

3D Quantitative and Qualitative Structure-Activity Relationships of the δ -Opioid Receptor Antagonists

Antagonists of the d -opioid receptor are effective in overcoming resistance against analgesic drugs such as morphine. To identify novel antagonists of the d -opioid receptor that display high potency and low resistance, we performed 3D-QSAR analysis using chemical feature-based pharmacophore models. Chemical features for d -opioid receptor antagonists were generated using quantitative (Catalyst/HypoGen) and qualitative (Catalyst/HipHop) approaches. For HypoGen analysis, we collected 16 peptide and 16 non-peptide antagonists as the training set. The best-fit pharmacophore hypotheses of the two antagonist models comprised identical features, including a hydrophobic aromatic (HAR), a hydrophobic (HY), and a positive ionizable (PI) function. The training set of the HipHop model was constructed with three launched opioid drugs. The best hypothesis from HipHop included four features: an HAR, an HY, a hydrogen bond donor (HBD), and a PI function. Based on these results, we confirm that HY, HAR and PI features are essential for effective antagonism of the d -opioid receptor, and determine the appropriate pharmacophore to design such antagonists.

Efficient 3D pharmacophore alignment as a tool for structure-based modeling and scoring

Chemical-feature based pharmacophore models have been established as state-of-the-art technique for bio-activity prediction. While in ligand-based drug design, chemical feature-based pharmacophore creation from a set of bio-active molecules is a frequently chosen approach, structure-based pharmacophores are still lacking the reputation to be an alternative or at least a supplement to docking techniques. Nevertheless, 3D pharmacophore screening bears the advantage of being much more efficient than unattended high-throughput docking and to transparently provide the user with all the information that is used by the screening algorithms to characterize the ligand-macromolecule interaction. Our efficient, rigid 3D pharmacophore superpositioning technique [1] will be applied to a number of structure-based pharmacophores selected from a diverse set of targets. Molecule coordinates generated from geometric fitting of multi-conformational models of small organic molecules to structure-based pharmacophores are compared to docking poses and discussed in terms of scoring, conformational coverage, and eligibility for virtual screening.

Influenza Virus Neuraminidase Inhibitors: Generation and Comparison of Structure‐Based and Common Feature Pharmacophore Hypotheses and Their Application in Virtual Screening.

X-ray crystallographic data of the influenza virus neuraminidase in complex with different inhibitors were used to generate chemical feature-based pharmacophore models of the binding site of this enzyme. The models were built using the software package Catalyst. Pharmacophore hypotheses derived from the 3-D structure of ligands cocrystallized with the enzyme were then compared with automatically generated common feature pharmacophore hypotheses for neuraminidase inhibitors. The latter models were found to contain fewer features and exhibited lower selectivity in virtual screening experiments. Some functions of the inhibitors obviously participate in more than one mode of interaction with the enzyme (charge-charge interaction and hydrogen bond) or form hydrogen bonds to several amino acids. Since such multiple interactions of one chemical function cannot be included into the Catalyst data format, strategies are presented to overcome these limitations. Finally, the results of 3-D database searching experiments using these hypotheses are described.