Molecular Field Analysis Approach Research Articles

Ligand-Based Design of Novel Quinoline Derivatives as Potential Anticancer Agents: An In-Silico Virtual Screening Approach.

In this study, using the Comparative Molecular Field Analysis (CoMFA) approach, the structure-activity relationship of 33 small quinoline-based compounds with biological anti-gastric cancer activity in vitro was analyzed in 3D space. Once the 3D geometric and energy structure of the target chemical library has been optimized and their steric and electrostatic molecular field descriptions computed, the ideal 3D-QSAR model is generated and matched using the Partial Least Squares regression (PLS) algorithm. The accuracy, statistical precision, and predictive power of the developed 3D-QSAR model were confirmed by a range of internal and external validations, which were interpreted by robust correlation coefficients (RTrain2=0.931; Qcv2=0.625; RTest2=0.875). After carefully analyzing the contour maps produced by the trained 3D-QSAR model, it was discovered that certain structural characteristics are beneficial for enhancing the anti-gastric cancer properties of Quinoline derivatives. Based on this information, a total of five new quinoline compounds were developed, with their biological activity improved and their drug-like bioavailability measured using POM calculations. To further explore the potential of these compounds, molecular docking and molecular dynamics simulations were performed in an aqueous environment for 100 nanoseconds, specifically targeting serine/threonine protein kinase. Overall, the new findings of this study can serve as a starting point for further experiments with a view to the identification and design of a potential next-generation drug for target therapy against cancer.

Synthesis, Characterization and Evaluation of Some Novel 2-Mercaptobenzothiazole Derivatives: In Silico and Experimental Approach

Recently, QSAR technique and molecular docking have become widespread in designing new series of compounds, depending upon the different models developed, before actually synthesizing and testing them. Due to the diverse activity profile of substituted Benzothiazole, it has become a pharmacophore of interest. The main motive of the study was to design some novel 2- Mercaptobenzothiazole molecules with potential anti-inflammatory effect. Thus, in order to get a better insight into the structural features of 2- Mercaptobenzothiazole, a series of analogues were taken from the literature and 2D, 3D QSAR models were developed and molecular docking and ADME studies were also performed. For 2D QSAR study, stepwise forwarding variable selection method, coupled with Partial Least Square (SWF-PLS) regression-based method, was used to generate the best 2D model giving r2= 0.9425, q2=0.9018, F-value=90.0118, pred_r2=0.8976. In contrast, the forward stepwise variable selection k-nearest neighbour molecular field analysis approach was used to generate best 3D QSAR model (q2=0.8171 and pred_r2=0.7006). Docking studies were performed on 29 derivatives of 2-Mercaptobenzothiazoles. The ligands were prepared and docked against protein S. aureus TYrRS with a resolution of 3.2Å. The three derivatives K6, K18 and K20 were found to have the best docking scores (-48.44, -47.46, -48.52). Further, synthesis of these three derivatives was carried out. Spectral characterization was carried out by IR, 1H NMR and 13C NMR and all the structures were found to be in agreement with the data obtained. In vitro anti-inflammatory evaluation of these compounds was carried out by following the protein denaturation method and HRBC membrane stabilization method. Compound K20 displayed potential anti-inflammatory activity, which may be attributed to the electronegative group on the phenyl ring substituted on the pyrazole part of the pharmacophore.

A Combined Approach of Pharmacophore Modeling, QSAR Study, Molecular Docking and In silico ADME/Tox Prediction of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one Analogs to Identify Potential Reverse Transcriptase Inhibitor: Anti-HIV Agents.

Reverse transcriptase is an important therapeutic target to treat AIDS caused by the Human Immunodeficiency Virus (HIV). Despite many effective anti-HIV drugs, reverse transcriptase (RT) inhibitors remain the cornerstone of the drug regimen to treat AIDS. In the present work, we have expedited the use of different computational modules and presented an easy, costeffective, and high throughput screening method to identify potential reverse transcriptase inhibitors. A congeneric series of 4-Arylthio & 4-Aryloxy-3- Iodopyridine-2(1H)-one analogs having anti-HIV activity were subjected to structure-based 2D, 3D QSAR, Pharmacophore Modeling, and Molecular Docking to elucidate the structural properties required for the design of potent HIV-RT inhibitors. Prediction of preliminary Pharmacokinetic and the Drug Likeliness profile was performed for these compounds by in silico ADME study. The 2D and 3D- QSAR models were developed by correlating two and three-dimensional descriptors with activity (pIC50) by sphere exclusion method and k-nearest neighbor molecular field analysis approach, respectively. The significant 2D- QSAR model developed by Partial Least Square is associated with the Sphere Exclusion method (PLS-SE), having r2 and q2 values 0.9509 and 0.8038, respectively. The 3D-QSAR model by Step Wise variable selection method (SW-kNN MFA) is more significant, which has a cross-validated squared correlation coefficient q2= 0.8509 and a non-crossvalidated correlation coefficient pred_r2= 0.8102. The pharmacophore hypothesis was developed, which comprised 5 features includes 3 aliphatic regions (Ala), 1 H-bond donor (HDr) and 1 H-bond acceptor (HAc). Docking studies of the selected inhibitors with the active site of reverse transcriptase enzyme showed hydrogen bond and π - π interaction with LYS-101, LYS-103, TYR- 181, TYR-188 and TRP-229 residues present at the active site. All the candidates with good bioavailability and ADMET drug likeliness properties. The results of the present work provide more useful information and important structural insights for the discovery, design of novel and potent reverse transcriptase inhibitors with high therapeutic windows in the future.

Quantitative Structure-Activity Relationships of Natural-Product-Inspired, Aminoalkyl-Substituted 1-Benzopyrans as Novel Antiplasmodial Agents.

On the basis of the finding that various aminoalkyl-substituted chromene and chromane derivatives possess strong and highly selective in vitro bioactivity against Plasmodium falciparum, the pathogen responsible for tropical malaria, we performed a structure–activity relationship study for such compounds. With structures and activity data of 52 congeneric compounds from our recent studies, we performed a three-dimensional quantitative structure–activity relationship (3D-QSAR) study using the comparative molecular field analysis (CoMFA) approach as implemented in the Open3DQSAR software. The resulting model displayed excellent internal and good external predictive power as well as good robustness. Besides insights into the molecular interactions and structural features influencing the antiplasmodial activity, this model now provides the possibility to predict the activity of further untested compounds to guide our further synthetic efforts to develop even more potent antiplasmodial chromenes/chromanes.

2D and 3D QSAR Studies of Saponin Analogues as Antifungal Agents against Candida albicans

: Objectives: Present communication deals with two- and three-dimensional (2D and 3D) QSAR studies of twenty saponin analogue for antifungal activity against Candida albicans. Methods: The 2D-QSAR model for the prediction was obtained by applying Multiple Linear Regression (MLR) method, giving r2 = 0.8551 and q2 = 0.7717 and Partial Least Squares (PLS) method, giving r2 = 0.8551 and q2 = 0.7717. 3D-QSAR study was performed using the stepwise variable selection k-nearest neighbour molecular field analysis (kNN-MFA) approach for both electrostatic and steric fields. Two different kNN-MFA methods (SA and GA) were used for the building of 3D-QSAR models. Results: The best model shows interesting result in terms of internal (q2 > 0.62) and external (predictive r2 > 0.52) predictivity for training and test set. Conclusion: Thus, QSAR models showed that hydrophobic and electrostatic effects dominantly determine the binding affinities. Hence the QSAR models proposed in this work would be further useful for development of new antifungal agents from medicinal plants and can help in the design of novel potent molecule.Key words: Saponin, QSAR, MLR, PLS, Candida.

THREE-DIMENSIONAL QSAR MODELING BENZIMIDAZOLE ANALOGUES USING THE K-NEAREST NEIGHBOR METHOD

We undertook the three-dimensional (3D) QSAR studies of a series of benzimidazole analogues to elucidate the structural properties required for angiotensin II. The 3D-QSAR studies were performed using the stepwise, simulated annealing (SA) and genetic algorithm (GA) selection k-nearest neighbor molecular field analysis approach; a leave-one-out cross-validated correlation coefficient q2 = 0.8216 and a pred_r2 = 0.7852 were obtained. The 3D QSAR model is expected to provide a good alternative to predict the biological activity prior to synthesis as antihypertensive agents.

3D QSAR AND DOCKING STUDY OF INDOLE DERIVATIVES AS SELECTIVE COX-2 INHIBITORS

Objective: Non-steroidal anti-inflammatory agents (NSAIDs) continue to be one of the most widely used groups of therapeutic agents. QSAR (quantitative structure-activity relationship) approach is a very useful and widespread technique for drug design. 3D QSAR facilitates evaluation of three-dimensional molecular fields around molecules and generates a relationship of these fields' values with the activity.
 Methods: 3D QSAR study was performed on selected twenty-four compounds from synthesized indole derivatives using the stepwise variable selection k-nearest neighbor (kNN) molecular field analysis approach for indicating the contribution of the steric and electronic field for activity. The docking study was performed to further confirm the binding affinity of synthesized molecules (ligands) to COX-2 enzyme as well as to study binding nature.
 Results: Statistically significant model was generated using VLife Molecular Design Suite 3.5 software with cross-validated correlation coefficient q2 of 0.9461 and high predictive correlation coefficient (Pred_r2) of 0.8782 indicating that the model is robust. The results of docking study suggest that the synthesized compounds have a comparable binding affinity with the COX-2 enzyme.
 Conclusion: The present study may prove to be helpful in the development and optimization of existing indole derivatives as anti-inflammatory agents with selective COX-2 inhibition.

KNN-MFA-guided 3D-QSAR on Some PDE4 Inhibitors of Benzylamine derivatives For Chronic Obstructive Pulmonary Disease

Benzylamine derivatives have recently been demonstrated interesting potential as the treatment for chronic obstructive pulmonary disease via interaction with the PDE4 enzymes. To understand the necessity around the nucleus k-nearest-neighbour molecular field analysis-based 3D-QSAR analysis was performed on a series of 47 compounds. The 3D-QSAR studies were performed using stepwise variable selection k-nearest-neighbour molecular field analysis approach; a leave-one-out cross-validated correlation coefficient (q2) of 0.8805 and a predicted r2 for the external test (pred_r2) of 0.6677 were obtained. Points generated in k-nearest-neighbour molecular field analysis 3D-QSAR model were S_1789 (-0.0023, -0.0021), E_14 (0.0271, 0.03699). Results showed positive range indicating that positive electrostatic potential is favourable for increase in the activity and hence a less electronegative substituent group is preferred in lattice point 14 (around R1). Negative range indicates that negative steric potential is favourable for increase in the activity and hence less bulky substituent group is preferred at lattice point. The information rendered by 3D-QSAR models lead to a better understanding of structural requirements for PDE4 inhibitors and help in the design of novel potent molecules.

PREDICTIVE QSAR MODELING OF PYRIDAZINYL DERIVATIVES USING K-NEAREST NEIGHBOR AND PHARMACOPHORE APPROACH

This study was carried out elucidate the structural properties required for pyridazinyl derivatives to exhibit angiotensin II receptor activity. The best 2D-QSAR model was selected, having correlation coefficient r2 = 0.8156, cross validated squared correlation coefficient q2 = 0.7348 and predictive ability of the selected model was also confirmed by leave one out cross validation method. Further analysis was carried out using 3D-QSAR method k-nearest neighbor molecular field analysis approach; a leave-one-out crossvalidated correlation coefficient of 0.7188 and a predictivity for the external test set (0.7613) were obtained. By studying the QSAR models, one can select the suitable substituent for active compound with maximum potency.

Prospective QSAR-Based Prediction Models with Pharmacophore Studies of Oxadiazole-Substituted α-Isopropoxy Phenylpropanoic Acids with Dual Activators of PPARα and PPARγ.

A series of oxadiazole-substituted [Formula: see text]-isopropoxy phenylpropanoic acids with dual activators of PPARα and PPARγ derivatives were subjected to two-dimensional and k-Nearest Neighbors molecular field analysis. The statistically significant best 2D-QSAR (PPARα ) model having good predictive ability with statistical values of r(2) = 0:8725; q(2) = 0:7957and pred_r(2) = 0:8136 was developed by GA-PLS with the descriptors like SsClcount, SddsN (nitro) count and SsOHcount that contribute significantly to the biological activity. The best 3D-QSAR studies (PPARα ) were performed using the genetic algorithm selection k-nearest neighbor molecular field analysis approach; a leave-one-out cross-validated correlation coefficient q(2) = 0:7188 and predicate activity pred_r(2) = 0.7508 were obtained. The influences of steric and electrostatic field effects generated by the contribution plots are discussed. The best pharmacophore model includes three features, viz. hydrogen bond donor, hydrogen bond acceptor and aromatic features. The information rendered by 2D-QSAR and 3D-QSAR models may lead to a better understanding of structural requirements of substituted α-isopropoxy phenylpropanoic derivatives and also aid in designing novel potent PPARα and PPARγ for antihyperglycemic molecules.

Prospective QSAR-based prediction models with pharmacophore studies of oxadiazole-substituted α-isopropoxy phenylpropanoic acids on with dual activators of PPARα and PPARγ

A series of oxadiazole-substituted α-isopropoxy phenylpropanoic acids with dual activators of PPARα and PPARγ derivatives were subjected to two dimensional and k-nearest neighbour Molecular field analysis. The statistically significant best 2D-QSAR (PPARα) model having good predictive ability with statistical values of r2 = 0.8725, q2 = 0.7957 and pred_r2 = 0.8136, was developed by GA-PLS with the descriptors like SsClcount, SddsN (nitro) count and SsOHcount contribute significantly to the biological activity. The best 3D-QSAR studies (PPARα) were performed using the genetic algorithm selection k-nearest neighbor molecular field analysis approach; a leave-one-out cross-validated correlation coefficient q2=0.7188 and predicate activity pred_r2 =0.7508 were obtained. The influences of steric and electrostatic field effects generated by the contribution plots are discussed. The best pharmacophore model includes three features viz. hydrogen bond donor, hydrogen bond acceptor, and aromatic features were developed. The information rendered by 2D, 3D QSAR models may lead to a better understanding of structural requirements of substituted α-isopropoxy phenylpropanoic derivatives and also aid in designing novel potent PPARα and PPARγ for antihyperglycemic molecules.

Comparative molecular field analysis of fenoterol derivatives interacting with an agonist-stabilized form of the β2-adrenergic receptor

The β2-adrenergic receptor (β2-AR) agonist [3H]-(R,R′)-methoxyfenoterol was employed as the marker ligand in displacement studies measuring the binding affinities (Ki values) of the stereoisomers of a series of 4′-methoxyfenoterol analogs in which the length of the alkyl substituent at α′ position was varied from 0 to 3 carbon atoms. The binding affinities of the compounds were additionally determined using the inverse agonist [3H]-CGP-12177 as the marker ligand and the ability of the compounds to stimulate cAMP accumulation, measured as EC50 values, were determined in HEK293 cells expressing the β2-AR. The data indicate that the highest binding affinities and functional activities were produced by methyl and ethyl substituents at the α′ position. The results also indicate that the Ki values obtained using [3H]-(R,R′)-methoxyfenoterol as the marker ligand modeled the EC50 values obtained from cAMP stimulation better than the data obtained using [3H]-CGP-12177 as the marker ligand. The data from this study was combined with data from previous studies and processed using the Comparative Molecular Field Analysis approach to produce a CoMFA model reflecting the binding to the β2-AR conformation probed by [3H]-(R,R′)-4′-methoxyfenoterol. The CoMFA model of the agonist-stabilized β2-AR suggests that the binding of the fenoterol analogs to an agonist-stabilized conformation of the β2-AR is governed to a greater extend by steric effects than binding to the [3H]-CGP-12177-stabilized conformation(s) in which electrostatic interactions play a more predominate role.

Exploring QSAR studies on 4-substituted quinazoline derivatives as antimalarial compounds for the development of predictive models

Quantitative structure activity relationship (QSAR) model for antimalarial activity was developed from a set of 51 substituted quinazolines that exhibited remarkable in vitro activity against sensitive and multidrug-resistant Plasmodium falciparum malaria. 2D-QSAR was done using partial least squares method coupled with stepwise variable selection; subsequently, 3D-QSAR was carried out using stepwise variable selection k-nearest neighbor molecular field analysis (kNNMF) approach. Leave-one-out cross-validated correlation coefficient (q2) of 0.7601 and a predicted r2 for the external test (pred_r2) of 0.7108 were obtained with 3D QSAR. Experimental results revealed that the molecular weight, alignment-independent descriptors, electrostatic and steric field descriptors were significantly correlated with antimalarial activity of quinazoline derivatives. The results helped to understand the nature of substituents around quinazoline nucleus, thereby providing new guidelines for the design of novel antimalarials.

3D-QSAR Studies on a Series of 2,4-Thiazolidinedione Derivatives: A Self- Organizing Molecular Field Analysis Approach to Design Novel PTP 1B Inhibitors

Diabetes mellitus is a group of metabolic disorders characterised by chronic hyperglycemia resulting either from a deficiency of insulin, or decreased ability to transduce the insulin signal, or both. Insulin resistance and β-cell dysfunction are two fundamental defects known to precede the onset of type 2 diabetes. PTP 1B is considered to function as a negative regulator of insulin signal transduction by dephosphorylating phosphotyrosine residues. 2,4-Thiazolidinediones (TZDs) have long been considered as antihyperglycemic agents which act by ameliorating insulin resistance and thereby normalizing elevated blood glucose level. A three dimensional quantitative structure-activity relationship (3D-QSAR) study was performed on a novel class of thiazolidinedione derivatives using self-organizing molecular field analysis (SOMFA) to correlate their molecular architecture with observed PTP 1B inhibitory activities. The master grid maps derived from the best model were used to display the contribution of both electrostatic and shape potential that can be mapped back onto structural features relating to the trends in inhibitory activities. The present SOMFA study indicated the indispensable molecular features which can be further explored for structural modifications of these lead molecules in order to optimize PTP 1B inhibitory activity.

Molecular Modeling Studies of Substituted 2,4,5‐Trisubstituted Triazolinones Aryl and Nonaryl Derivatives as Angiotensin II AT1 Receptor Antagonists

The development of new therapies to treat hypertension and cardiovascular diseases. A series of 2,4,5‐trisubstituted triazolinones aryl and nonaryl derivatives were subjected to Group-based QSAR, k-nearest neighbour molecular field analysis, and pharmacophore mapping. Multiple linear regression (MLR) methodology coupled with feature selection method namely simulated annealing, was applied to derive Group based QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The best physicochemical descriptors, namely, R1chiV1, R2T_N_O_3, R2chlorines count, R2T_C_N_4, and R2SssNHE index, contribute significantly to the biological activity. The statistically significant best Group‐based QSAR model has r2 = 0.8357 and q2 = 0.7266 with pred_r2 = 0.8138. The 3D‐QSAR studies were performed using the simulated annealing selection k‐nearest neighbor molecular field analysis approach; a leave‐one‐out cross‐validated correlation coefficient q2 = 0.7461 and predicate activity pred_r2 = 0.7790 were obtained. Contour maps using this approach showed that steric, electrostatic, and hydrophobic effects dominantly determine binding affinities. Pharmacophore hypotheses were generated by the mol sign module and found to contain common features like hydrogen bond donor acceptor, donor, positive, negative ionizable, and hydrophobic features. This model can be used for preliminary screening of large number of substituted 3H‐1,‐2,‐4 triazolinone aryl and nonaryl derivatives. The information rendered by 3D‐QSAR models may lead to a better understanding of structural requirements of triazolinone aryl and nonaryl derivatives and also aid in designing novel potent antihypertensive molecules.

3D-QSAR and docking studies of arylmethylamine-based DPP IV inhibitors

The present work was focused on the study of the three-dimensional (3D) structural requirements for the highly potent bioactivity of dipeptidyl peptidase (DPP) IV's inhibitor. At first, molecular dynamic and mechanic (MD/MM) simulations were performed to research the conformations of the potent DPP IV's inhibitor 5-(aminomethyl)-6-(2,4-dichlorophenyl)-2-(3,5-dimethoxy-phenyl)pyrimidin-4-amine. Using the MD/MM-determined molecular conformers as templates, the 3D quantitative structure activity relationship (QSAR) studies were carried out based on a set of arylmethylamine DPP IV inhibitors with the comparative molecular field analysis (CoMFA) approach. The best 3D-QSAR model was constructed with good statistic values of rcv2 and R2 using PLS analyses (CoMFA: rcv2=0.660, R2=0.953). The generated 3D-QSAR model was proved to be reliable by internal and external validations. Docking studies were further performed to analyze the interaction mode between the highly potent or low potent arylmethylamine derivatives and DPP IV. Our flexible docking results also confirmed the possible bioactive conformation obtained from the 3D-QSAR model, of arylmethylamine-based DPP IV inhibitors. The 3D-QSAR model may provide information of pharmacophoric features for further design and optimization of new scaffold compounds with high inhibitory activity to DPP IV.

Comprehensive two and three-dimensional QSAR studies of 3-substituted 6-butyl-1,2dihydropyridin-2-ones derivatives as angiotensin II receptor antagonists

In this paper, an attempt was made to develop a two and three-dimensional QSAR models on a series of 3-substituted 6-butyl-1,2 dihydropyridin-2-ones derivatives acting as angiotensin II receptor antagonists using partial least squares regression method. Various 2D descriptors were calculated and used in the present analysis. For model validation, the dataset was divided into various training and test sets using random selection procedures. The whole dataset was divided into training set (24 compounds) and test set (5 compounds). The statistically significant best 2D-QSAR model having correlation coefficient (r 2 = 0.9062) was selected for further study. The model was further validated by means of crossed squared correlation coefficient (q 2 = 0.7314 and pred_r 2 = 0.8133) which shows model has good predictive ability. The optimum QSAR model showed that the parameters SdsCHE-index, T_N_N_3, fluorines count, and SaaNHE-index contributed highly positive, while SsCH3count contributed negatively for antihypertensive activity. Using kNNMFA approach, various 3D-QSAR models were generated and selected on the basis of q 2 and predictive r 2 values. 3D-QSAR study was performed using k-nearest neighbor molecular field analysis approach for steric, electrostatic, and hydrophobic fields. Molecular field analysis was used to construct the best 3D-QSAR model using simulated annealing method. The significant best 3D-QSAR model-6 having model with good external and internal predictivity for the training and test sets has shown cross-validation (q 2) and external validation (pred_r2) values of 0.8534 and 0.8020, respectively. The steric, electrostatic, and hydrophobic descriptors at the grid points S_415, S_1059, E_1523, E_1045, E_1308, and H_153 play an important role in the design of new molecule. This model was found to yield reliable clues for further optimization of 3-substituted 6-butyl-1,2dihydropyridin-2-ones in the data set. The information rendered by 2D- and 3D-QSAR models may lead to a better understanding of structural requirements of 3-substituted 6-butyl-1,2dihydropyridin-2-ones derivatives and also aid in designing novel potent antihypertensive molecules.

A QSAR Analysis of 2-phenoxy-N-substituted Acetamide Analogues as Hypoxia-Inducible Factor-1(HIF-1) Inhibitors: A Rational Approach to Anticancer Drug Design

A set of thirty one substituted 2-phenoxy-N-phenylacetamide derivatives with HIF-1 inhibitory activities was subjected to 2D and 3D Quantitative Structure Activity Relationship (QSAR) studies using various combinations of descriptors. 2D-QSAR was performed using Multiple Linear Regression (MLR), Principal Component Regression (PCR) and Partial Least Squares Regression (PLS) methods. Among these three methods Multiple Linear Regression (MLR) led to the statistically significant best 2D-QSAR Model-I having correlation coefficient r(2) = 0.9469 and cross validated squared correlation coefficient q(2) = 0.8933 with external predictive ability of pred_r(2) = 0.7128 with the descriptors like SssNHE-index, slogp, T_O_N_1 and T_2_Cl_1. 3D-QSAR study was performed using the simulated annealing variable selection procedures k-nearest neighbor molecular field analysis approach. 3D-QSAR shows interesting results in terms of internal and external predictability. Molecular field analysis was applied for the generation of steric, hydrophobic and electrostatic descriptors based on aligned structures which shows good correlative and predictive capabilities in terms of q(2) = 0.9672 and pred_r(2) = 0.8480. Hence the model proposed in this work provides important structural insight in designing novel derivatives with specific HIF-1 inhibitory activity.

Self-organizing molecular field analysis of NSAIDs: Assessment of pharmacokinetic and physicochemical properties using 3D-QSPkR approach

Non-steroidal anti-inflammatory drug (NSAID) induced gastrointestinal toxicity has attracted greater attention over the years. The development of NSAIDs having safer therapeutic profile depends on the better understanding of their mechanisms, physicochemical and pharmacokinetic properties. The present investigation is aimed at in silico three dimensional quantitative structure–pharmacokinetic relationship (3D-QSPkR) assessment of a group of NSAIDs using self-organizing molecular field analysis (SOMFA) approach. Two different statistically validated models for dissociation constant (pKa) and volume of distribution (Vd) were obtained from SOMFA studies of 22 clinically active NSAIDs having diversity in structure. 3D-QSPkR models delivered useful information about the contribution of shape and electrostatic potential on pKa and Vd. The study illustrated the significance of structural variables in molecular architecture of NSAIDs especially etodolac for further optimization of ADMET properties with improved therapeutic profile.

2D, 3D-QSAR and docking studies of 1,2,3-thiadiazole thioacetanilides analogues as potent HIV-1 non-nucleoside reverse transcriptase inhibitors

BackgroundThe discovery of clinically relevant inhibitors of HIV-RT for antiviral therapy has proven to be a challenging task. To identify novel and potent HIV-RT inhibitors, the quantitative structure–activity relationship (QSAR) approach became very useful and largely widespread technique forligand-based drug design.MethodsWe perform the two- and three-dimensional (2D and 3D) QSAR studies of a series of 1,2,3-thiadiazole thioacetanilides analogues to elucidate the structural properties required for HIV-RT inhibitory activity.ResultsThe 2D-QSAR studies were performed using multiple linear regression method, giving r2 = 0.97 and q2 = 0.94. The 3D-QSAR studies were performed using the stepwise variable selection k-nearest neighbor molecular field analysis approach; a leave-one-out cross-validated correlation coefficient q2 = 0.89 and a non-cross-validated correlation coefficient r2 = 0.97 were obtained. Docking analysis suggests that the new series have comparable binding affinity with the standard compounds.ConclusionsThis approach showed that hydrophobic and electrostatic effects dominantly determine binding affinities which will further useful for development of new NNRTIs.