Pharmacophore Studies Research Articles

Identification of 3-Nitro-2,4,6-trihydroxybenzamide Derivatives as Photosynthetic Electron Transport Inhibitors by QSAR and Pharmacophore Studies.

In the present investigation, quantitative structure-activity relationship (QSAR) analysis was performed on a data set consisting of structurally diverse compounds in order to investigate the role of their structural features on their photosynthetic electron transport Inhibitors. The best 2D-QSAR model was selected, having correlation coefficient r (2)=0.8544 and cross-validated squared correlation coefficient q (2)=0.7139 with external predictive ability of pred_r (2)=0.7753. The results obtained in this study indicate that the presence of hydroxy and nitro groups, expressed by the SsOHcount and SddsN (nitro) count, is the most relevant molecular property determining efficiency of photosynthetic inhibitory. Molecular field analysis was used to construct the best k-nearest neighbor (kNN-MFA)-based 3D-QSAR model using SA-PLS method, showing good correlative and predictive capabilities in terms of [Formula: see text] and [Formula: see text]. The pharmacophore model includes three features viz. hydrogen bond donor, hydrogen bond acceptor, and one aromatic feature. The developed model was found to be predictive and can be used to design potent photosynthetic electron transport activities prior to their synthesis for further lead modification.

A comparative QSAR analysis of substituted imidazolones derivatives as angiotensin II AT1 receptor antagonists

Two-dimensional (2D), Three-dimensional (3D) quantitative structure–activity relationship (QSAR) and Pharmacophore studies of angiotensin II AT1 receptor antagonists substituted imidazolones derivatives have been carried out. Statistically significant best 2D-QSAR and k-nearest neighbor-based 3D-QSAR model were generated on q2 = 0.7118; 0.7159 with pred_r2 = 0.8313, 0.7377, respectively, was developed by genetic algorithm-PLS techniques. The pharmacophore model generated consisting of the five-point hypotheses contains two HAc (hydrogen bond acceptor) site, one negative ionizable and two AroC feature (aromatic) region. On the basis of generated models, a statistically valid 2D and 3D-QSAR with good predictability was developed. All molecular modeling studies were compared to each other. The current study may help in designing novel Angiotensin II AT1 receptor.

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.

Virtual screening and synthesis of novel antitubercular agents through interaction-based pharmacophore and molecular docking studies.

Tuberculosis continues to become a major threat and wide spreading disease though out the world. Therefore it is required to identify the new drugs for the treatment of tuberculosis with better activity profile than the prevalent compounds. In present study we have screened and modified the antitubercular compounds from commercial chemical database using the interaction-based pharmacophore and molecular docking studies. In the first step different pharmacophores of cocrystal structures of enyol acyl carrier reductase (also known as InhA) proteins (2B36 and 3FNG) were generated and employed for screening of ChemDiv database. Four different pharmacophore hypothesis retrieved 3456 hits from approximately 0.67 million compounds. In the second filter, these hit molecules were subjected to the molecular docking studies in 2NSD and 3FNG crystal structures. On the basis of high fit values, GScore, structural diversity and visual inspection, one hundred compounds were selected, purchased and subjected to experimental validation for antitubercular activity against H37Rv Mycobacterium tuberculosis (MTB) strain. Three compounds showed the minimal inhibitory concentration (MIC) value at 16 μg/mL and one compound VH04 showed the value at 1 μg/mL. Then a more active amidoethylamine compound was developed by chemical modifications of the virtual hit VH04 against the MTB strain. We believe that this newly identified scaffold could be useful for the optimization of lead from hit compounds of new antitubercular agents.

A comprehensive immunoinformatics and target site study revealed the corner-stone toward Chikungunya virus treatment

Recent concerning facts of Chikungunya virus (CHIKV); a Togaviridae family alphavirus has proved this as a worldwide emerging threat which causes Chikungunya fever and devitalizing arthritis. Despite severe outbreaks and lack of antiviral drug, a mere progress has been made regarding to an epitope-based vaccine designed for CHIKV. In this study, we aimed to design an epitope-based vaccine that can trigger a significant immune response as well as to prognosticate inhibitor that can bind with potential drug target sites by using various immunoinformatics and docking simulation tools. Initially, whole proteome of CHIKV was retrieved from database and perused to identify the most immunogenic protein. Structural properties of the selected protein were analyzed. The capacity to induce both humoral and cell-mediated immunity by T cell and B cell were checked for the selected protein. The peptide region spanning 9 amino acids from 397 to 405 and the sequence YYYELYPTM were found as the most potential B cell and T cell epitopes respectively. This peptide could interact with as many as 19 HLAs and showed high population coverage ranging from 69.50% to 84.94%. By using in silico docking techniques the epitope was further assessed for binding against HLA molecules to verify the binding cleft interaction. In addition with this, the allergenicity of the epitopes was also evaluated. In the post therapeutic strategy, three dimensional structure was predicted along with validation and verification that resulted in molecular docking study to identify the potential drug binding sites and suitable therapeutic inhibitor against targeted protein. Finally, pharmacophore study was also performed in quest of seeing potent drug activity. However, this computational epitope-based peptide vaccine designing and target site prediction against CHIKV opens up a new horizon which may be the prospective way in Chikungunya virus research; the results require validation by in vitro and in vivo experiments.

Ligand based analysis on HMG-CoA reductase inhibitors

In the present investigation, we have performed ligand based analyses (QSAR (binary classification and multiple linear regression (MLR) analyses), pharmacophore studies and fingerprint analysis) on a data set comprised of 249 HMG CoA reductase (HMGR) inhibitors. The QSAR and the pharmacophore analyses explained that the polar features present in the molecules determine the observed HMGR inhibitory activity of the compounds. The binary classification study performed with bundle of descriptors such as PEOE_PC-, GCUT_PEOE-0, GCUT_PEOE-3, MNDO_LUMO, VSA_Pol and V_surf-ID7 descriptors in model A and TPSA, LogP(o/w), LogS, Wienerpol, A_don and A_acc descriptors belong to the model B. The binary classification study provided acceptable statistical parameter values for accuracy, sensitivity, specificity, precision, Matthew's correlation coefficient (MCC), goodness of fit score (GH score), F-score (the harmonic mean of precision and sensitivity), enrichment factor, etc., against both models (A and B), which demonstrated that these models predict the activities of the compounds (as active and inactive) significantly. The sum of ranking difference (SRD) was calculated using statistical parameters derived from the models (MLR-QSAR and binary QSAR models). The %SRD reveals that the model 5 in MLR-QSAR and the binary models A1, B1 and A2 are significant among the developed models. Further protein ligand interaction fingerprints (PLIF) and the pharmacophore studies also revealed that the hydrogen bond acceptor, hydrogen bond donor and ionic interactions are involved in the protein ligand interaction to elicit the activity of the molecules. In order to support the findings from the analyses, the MACCS fingerprints of the data set were calculated and which explain that the polar functional groups/fragments in the molecules are important for the inhibitory activity. The results retrieved from these analyses will be useful for the development of novel molecules for the inhibition of HMGR enzyme.

The 3D-QSAR and pharmacophore studies of pyrimidine derivatives as HCV replication (replicase) inhibitor

Three-dimensional quantitative structure activity relationship has been carried out on a series of 211 pyrimidine derivatives as HCV replication (replicase) inhibitors to identify the chemical structural impacting the biological activities. The QSAR models based on 157 compounds screened as the training set and the rest as the test set were established by comparative molecular field analysis and molecular similarity indices analysis (CoMSIA) methods. The optimal CoMSIA model exhibits both satisfying internal and external prediction performance with q2 = 0.501, \( R^{ 2}_{\text{ncv}} \; = \; 0. 9 6 2 \), \( R^{ 2}_{\text{pred}} \; = \; 0. 8 8 3 \). In addition, the 3D contour map analysis and the pharmacophoric features identified from corresponding pharmacophore study point out the key structural features that affect the inhibition activity of the pyrimidine analogs that (1) bulky substituents at position 11 and 23 are unfavorable; (2) minor positive 16 charges and H-bond donor groups at this position are benefit while H-bond acceptor near this position are detrimental; (3) substituents near Ring B: minor H-bond donor, H-bond acceptor groups are favorable; (4) electro-negative charges at position 7 and 9 are favorable while H-bond donor at position 9 are unfavorable; (5) two H-bond donor sites (DS1, DS2), two H-bond donor atoms (DA1, DA2); two H-bond acceptor atoms (AA1, AA2), hydrophobic and aromatic, and aromatic centers account for the pharmacophore features of the model.

Identification of 3-nitro-2, 4, 6-trihydroxybenzamide derivatives as photosynthetic electron transport inhibitors by QSAR and pharmacophore studies.

In the present investigation, QSAR analysis was performed on a data set consist of structurally diverse compounds in order to investigate the role of its structural features on their Photosynthetic Electron Transport Inhibitors. The herbicidal activity co-related with certain topological and hydrophobicity based descriptors, 3D descriptors dependent steric, electrostatic and hydrophobic. The best 2D QSAR model was selected, having correlation coefficient r2 = 0.8544 and cross validated squared correlation coefficient q2 = 0.7139 with external predictive ability of pred_r2 = 0.7753 was developed. The results obtained in this study indicate that hydroxy and nitro groups, as expressed by the SsOHcount, SddsN (nitro) count, is the most relevant molecular property determining efficiency of photosynthetic inhibitory. Molecular field analysis was used to construct the best k-nearest neighbor (kNN-MFA)-based 3DQSAR model using SA-PLS method, showing good correlative and predictive capabilities in terms of q2 = 0.7694 and pred_r2 = 0.7381. The influences of steric, electrostatic and hydrophobic field effects generated by the contribution plots are discussed. The pharmacophore model includes three features viz. hydrogen bond donor, hydrogen bond acceptor, and one aromatic feature was developed. The developed model was found to be predictive and can be used to design potent Photosynthetic Electron Transport activities prior to their synthesis for further lead modification. The results obtained suggest that the 3-nitro-2, 4, 6-trihydroxybenzamide analogues represent promising candidates for the development of new active principles targeting photosynthesis to be used as herbicides.

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.

Biologically Active Ligands for Yersinia Outer Protein H (YopH): Feature Based Pharmacophore Screening, Docking and Molecular Dynamics Studies

Yersinia pestis, a Gram negative bacillus, spreads via lymphatic to lymph nodes and to all organs through the bloodstream, causing plague. Yersinia outer protein H (YopH) is one of the important effector proteins, which paralyzes lymphocytes and macrophages by dephosphorylating critical tyrosine kinases and signal transduction molecules. The purpose of the study is to generate a three-dimensional (3D) pharmacophore model by using diverse sets of YopH inhibitors, which would be useful for designing of potential antitoxin. In this study, we have selected 60 biologically active inhibitors of YopH to perform Ligand based pharmacophore study to elucidate the important structural features responsible for biological activity. Pharmacophore model demonstrated the importance of two acceptors, one hydrophobic and two aromatic features toward the biological activity. Based on these features, different databases were screened to identify novel compounds and these ligands were subjected for docking, ADME properties and Binding energy prediction. Post docking validation was performed using molecular dynamics simulation for selected ligands to calculate the Root Mean Square Deviation (RMSD) and Root Mean Square Fluctuation (RMSF). The ligands, ASN03270114, Mol_252138, Mol_31073 and ZINC04237078 may act as inhibitors against YopH of Y. pestis.

Pharmacophore and docking-based 3D-QSAR studies on HIV-1 integrase inhibitors

Integrase(IN) plays an essential role in the process of HIV-1 replication. IN inhibitors of diketo acid derivatives( DKAs) were analysed by the Comparative Molecular Field Analysis(CoMFA) and Comparative Molecular Similarity Induces Analysis(CoMSIA) methods. A set of 42 compounds were randomly selected as the training set(35) and test set(7). Firstly, a good pharmacophore(goodness of hit=0.787) was obtained and used to align ligands. Then, predictive models were constructed with the CoMFA and CoMSIA methods based on the pharmacophore alignment. As a result, the CoMSIA method yielded the best model with an r 2 of 0.955 and a q 2 of 0.665, which can predict the activities of the tested DKAs very well(r 2=0.559). Finally, DKAs were docked into IN, and the predicit modes were superimposed on the contour maps obtained from the best CoMSIA model. The superimposed maps gave a visualized and meaningful insight into the inhibitory behaviors, providing significantly useful information for the rational drug design of anti-IN agents.

Interference of Boswellic Acids with the Ligand Binding Domain of the Glucocorticoid Receptor

Boswellic acids (BAs) possess anti-inflammatory properties in various biological models with similar features to those of glucocorticoids (GCs), such as suppression of the release of pro-inflammatory cytokines. Hence, the molecular mechanism of BAs responsible for their anti-inflammatory features might be attributable to interference with the human glucocorticoid receptor (GR). Due to obvious structural similarities with GCs, we conducted pharmacophore studies as well as molecular docking simulations of BAs as putative ligands at the ligand binding site (LBS) of the GR in distinct functional states. In order to verify receptor binding and functional activation of the GR by BAs, radiometric binding assays as well as GR response element-dependent luciferase reporter assay were performed with dexamethasone (DEX) as a functional positive control. With respect to the observed position of GCs in GR crystal complexes in the active antagonist state, BAs docked in a flipped orientation with estimated binding constants reflecting nanomolar affinities. For validation, DEX and other steroids were successfully redocked into their crystal poses in similar ranges as reported in the literature. In line with the pharmacophore and docking models, the BAs were strong GR binders (radiometric binding assay), albeit none of the BAs activated the GR in the reporter gene assay, when compared to the GC agonist DEX. The flipped scaffolds of all BAs dislodge the known C-11 function from its receiving amino acid (Asn564), which may explain the silencing effects of receptor-bound BAs in the reporter gene assay. Together, our results constitute a compelling example of rigid keys acting in an adaptable lock qualifying as a reversed induced fit mechanism, thereby extending the hitherto published knowledge about molecular target interactions of BAs.

Synthesis of PPAR-γ Activators Inspired by the Marine Natural Product, Paecilocin A

A series of N-substituted phthalimide derivatives were synthesized based on a pharmacophore study of paecilocin A (a natural PPAR-γ agonist) and synthetic leads. The introduction of hydrophilic and hydrophobic groups to the phthalimide skeleton yielded compounds 3–14. Compound 7 showed significant PPAR-γ activation in a luciferase assay using rat liver Ac2F cells. Docking simulations showed that a free hydroxyl group on the phthalimide head and a suitable hydrophilic tail, including a phenyl linker, were beneficial for PPAR-γ activation. Compound 7 and rosiglitazone concentration-dependently activated PPAR-γ with EC50 values of 0.67 μM and 0.028 μM, respectively. These phthalimide derivatives could be further investigated as a new class of PPAR-γ ligands.

3-phenylquinolinylchalcone derivatives: pharmacophore modelling, 3d-qsar analysis and docking studies as anti-cancer agents

Certain 3-Phenylquinolinylchalcone derivatives were evaluated for their anti-proliferative activities and found to exhibit anti-cancer and anti-inflammatory activities. 3D-QSAR and molecular docking approaches were performed on 3-Phenylquinolinylchalcone derivatives to understand their structural requisites and binding mode of the best fitted ligand for cancer inhibitory activity. Among them, (E)-3-(3-(4-methoxyphenyl)quinolin-2-yl)-1-phenylprop-2-en-1-one (6a) was the most active compound against the growth of H460, MCF-7, MDA-MB-231 and SKBR-3 cancer cell line respectively. Four featured hypothesis AHRR.521 of H460 was considered to be the best hypothesis which yielded a statistically significant 3D-QSAR model built with PLS values 3, Regression coefficient (R 2 ) = 0.8986, Cross validation coefficient (Q 2 ) = 0.9542, Root Mean Square Deviation (RMSD) = 0.0067, Pearson-R = 1. Interestingly, the result of docking was found to correlate with the pharmacophore study where this compound was active against all six oncoproteins p53, Raf Kinase, Aurora-A-Kinase, CDK-2, Resveratrol and HSP90 . The results provide detailed insights of 6a compound which can afford guidance for rational drug design of novel potent anti-cancer agents

Prediction of potent therapeutic anticonvulsant ligands to N-methyl-d-aspartate receptors among substituted 4,6-dichloroindole-2-carboxylic acids: Molecular modeling approach

We performed two- and three-dimensional quantitative structure–activity relation (QSAR) pharmacophore studies with a series of substituted 4,6-dichloroindole-2-carboxylic acid analogs in order to elucidate the structural properties required for high affinity for the glycine binding site of the N-methyl-d-aspartate receptor. The partial least-squares method coupled with various feature selection methods (stepwise, genetic algorithm and simulated annealing) were used to derive QSAR models, which were 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=0.8577, a cross-validated squared correlation coefficient, q2=0.7118 and an external predictive ability pred_r2=0.7642. Molecular field analysis was used to construct the best three-dimensional QSAR model by the genetic algorithm–partial least-squares method, which showed good correlational and predictive capability: q2=0.7538, q2_se=0.4833 and pred_r2=0.7019. In a series of 4,6-dichloroindole-2-carboxylic acid derivatives, the pharmacophore model based on the chemical feature with the lowest root mean square deviation (2.187nm) consisted of one aromatic feature, one hydrogen bond donor, one hydrogen bond acceptor, one aliphatic and one positive coefficient feature. A representative set of 4,6-dichloroindole-2-carboxylic acid compounds with effective biological activity and a good glycine binding site on the N-methyl-d-aspartate receptor were identified, which may be potential leads for drugs with anticonvulsant activity.

3D QSAR and pharmacophore study of curcuminoids and curcumin analogs: Interaction with thioredoxin reductase

Curcumin is the yellow pigment of Curcuma longa that irreversibly inhibits the activity of thioredoxin reductase (TrxR) and forms adduct. TrxR, a homodimeric enzyme with E and F chains, is responsible for redox control of the cell and defense against oxidative stress. It is also well known that TrxR is over-expressed in tumor cells. Hence TrxR is a promising target for curcumin based therapy. Binding site of TrxR for curcumin is at the interface of homodimers. In the present study, naturally occurring curcuminoids and forty four synthetic analogs of curcumin were docked with SP/XP glide suite and E-pharmacophore was simulated. E-pharmacophore of both chains has shown three donor features and one acceptor feature. 3D atom based QSAR models have been proposed for the two series of curcumin analogues of known IC50values. The data obtained indicates that the training set model is quite efficient to predict the test set of data. Obtained models and ADMET prediction could be employed for design and synthesis of more potent inhibitors of TrxR.

2D, 3D-QSAR, and pharmacophore studies on thiazolidine-4-carboxylic acid derivatives as neuraminidase inhibitors in H3N2 influenza virus

Thiazolidine-4-carboxylic acid derivative compounds with their inhibitory activity against influenza A neuraminidase (NA) were used as a data set for developing the quantitative structure–activity relationship (QSAR) model. The 2D-QSAR model was developed using multiple linear regression analysis with r 2 and r 2 (CV) value of 0.98 and 0.70, respectively. The generated QSAR model has shown that the electrostatic and steric properties have the predominant influence on biological activity. 3D-QSAR was modeled with r 2 value of 0.98 and RMSD of 0.12. The pharmacophore alignments were generated and suggested that the hydrophobic, hydrogen bond donor, and hydrogen bond acceptor features on R1 and R2 site substitutions of the core of thiazolidine are important properties to enhance the activities of molecule against influenza A NA.

3D-QSAR using pharmacophore-based alignment and virtual screening for discovery of novel MCF-7 cell line inhibitors

The development of a novel approach for the prediction of antiestrogenic activity is described, bringing up to date a previous pharmacophore study. Software Phase has been used to derive a 3D-QSAR model based, as alignment rule, on a pharmacophore built on three compounds highly active against MCF-7 cell line. Five features comprised the pharmacophore: two hydrogen-bond acceptors, one hydrogen-bond donor, and two aromatic rings. The sequential 3D-QSAR yielded a test set q2 equal to 0.73 and proved to be predictive with respect to an external test set of 21 compounds (r2 = 0.69). The model was used to detect new MCF-7 inhibitors through 3D-database searching and identified fourteen compounds that were subsequently tested in vitro against the MCF-7 human breast adenocarcinoma cell line. Eleven out of the fourteen compounds exhibited inhibitory activity with IC50 values ranging between 30 and 186 μM. The results of the study confirmed the fundamental validity of the chosen approach as a hit discovery tool.

Pharmacophore and QSAR modeling of some structurally diverse azaaurones derivatives as anti-malarial activity

Quantitative structure–activity relationship (QSAR) and Pharmacophore studies were performed on a series of 35 azaaurones derivatives to find out the structural requirements of their antimalarial activities. The compounds in the selected series were characterized by spatial, molecular and electrotopological descriptors using QSAR module of molecular design suite (V-Life MDSTM 3.5). Correlations between inhibitory activities and calculated predictor variables were established through partial least square regression method. The developed QSAR models were found to be statistically significant with respect to training (r 2 > 0.6), and cross-validation (q 2 > 0.6). Simulated annealing (SA) and stepwise (SW) regression are applied as variable selection methods for an effective comparison and model development. This study was performed with 35 compounds (data set) using the sphere exclusion (SE) algorithm method for the division of the data set into training and test sets. The statistically significant 2D-QSAR model having r 2 = 0.9061 and q 2 = 0.8150 with pred_r 2 = 0.8719 was developed by SW-PLS confirms a positive contribution of T_O_Cl_6, T_C_Cl_1, SsOHcount, T_C_F_2 and SsCH3E index to the anti-malarial activity and best Group-based QSAR (GQSAR) model having r 2 = 0.7624 and q 2 = 0.7341 with pred_r 2 = 0.7461 was developed by SA-PLS. The result two-dimensional QSAR and GQSAR clearly explained that substitution of topological indices, hydrophobic properties and auto-correlation descriptors of different atomic properties at R1 and R2 on aurone ring is essential for the activity. Further analysis using three-dimensional QSAR technique identifies a suitable model obtained by SA-partial least square method leading to anti-malarial activity prediction. Molecular field analysis was used to construct the best 3D-QSAR model using k-nearest neighbor method, showing good correlative and predictive capabilities in terms of q 2 = 0.6906 and pred_r 2 = 0.7454. Additionally the pharmacophore model well corroborated with k-nearest neighbor studies as the contours of later were in good agreement with the 3D orientation of the pharmacophoric features. The distances between the pharmacophore sites were measured in order to confirm their significance to the activities. The results reveal that the acceptor, donor, and aromatic/hydrophobic pharmacophore properties are favorable contours sites for the activities. The results of 2D-QSAR, GQSAR and k-nearest neighbor-based 3D-QSAR studies give detailed structural insights as well as highlights important binding features of these substituted azaaurones derivatives as anti-malarial activity, which can provide guidance for the rational design of novel potent azaaurones derivatives.

SAR and Pharmacophore Based Designing of Some Antimalarial and Antiretroviral Agents: An INTERNET Based Drug Design Approach

With the development of computational chemistry and molecular docking studies, Structure Activity Relationship or SAR- and pharmacophore-based drug design have been modified to target based drug discovery using sophisticated computational tools which is not very much user friendly and has got many incompatibility issues with many operating systems (OS) and other system configurations. In this paper SAR and pharmacophore based drug design approaches have been described by the used of free internet based tools which are very much user friendly and can almost compatible with any platform. Some antimalarial. And anti retroviral agents have been designed using pharmacophore study and their drug like properties, toxicity, metabolic sites and other parameters are predicted by the free internet based tools.