3D QSAR Studies on Benzoxazoles and Oxazolo-(4, 5-b)pyridines as Anti-fungal agents

2009 ◽  
Vol 2 (2) ◽  
pp. 557-565
Author(s):  
Shaheen Begum ◽  
Satya Parameshwar K ◽  
Ravindra G K ◽  
Achaiah G
Keyword(s):  
Hydrogen Bond ◽  
Antifungal Activity ◽  
Drug Research ◽  
3D Qsar ◽  
Qsar Model ◽  
Hydrogen Bond Acceptor ◽  
Test Set ◽  
Qsar Studies ◽  
The Cost ◽  
Anti Fungal

Benzoxazoles and Oxazolo-[4,5-b]pyridines  have been reported as potent anti-fungal agents. 3D QSAR tools including CoMFA and CoMSIA have been known to be a promising approaches is to correlate structures and activity which further enable the medicinal chemists to design more potent molecules thus curtailing the cost and time in drug research. CoMFA and CoMSIA studies have been carried out on 31 molecules of benzoxazole and oxazolopyridines in order to determine the structural properties required for effective antifungal activity. 26 compounds were evaluated for establishing QSAR model, which was then validated by predicting the activities of five test set molecules. All the molecules were aligned by SYBYL database alignment which led to a best model with q2 value of 0.835, r2=0.976 and r2pred=0.773. This model was further employed to derive CoMSIA models, a best model with steric, electrostatic, hydrophobic and hydrogen bond acceptor indices exhibited q2 = 0.812, r2=0.971 and r2pred=0.81. The models thus obtained from this study can be useful for the design and development of new potential anti-fungal agents.

Improved 3D-QSAR Prediction by Multiple-Conformational Alignments and Molecular Docking Studies to Design and Discover HIV-I Protease Inhibitors

2020 ◽  
Vol 18 ◽  
Author(s):  
Paresh K. Patel ◽  
Hardik G. Bhatt
Keyword(s):  
Hydrogen Bond ◽  
Drug Design ◽  
Protease Inhibitors ◽  
Correlation Coefficient ◽  
3D Qsar ◽  
Qsar Model ◽  
Hydrogen Bond Acceptor ◽  
Contour Maps ◽  
Validation Parameters ◽  
Predicted Values

Background: Inhibition of HIV-I protease enzyme is a strategic step for providing better treatment in retrovirus infections which avoids resistance and has less toxicities. Objectives: In the course of our research to discover new and potent protease inhibitors, 3D-QSAR (CoMFA and CoMSIA) models were generated using 3 different alignment techniques including multifit alignment, docking based and Distill based alignment for 63 compounds. Novel molecules were designed from the output of this study Methods: Total 3 alignment methods were used to generate CoMFA and CoMSIA models. A Distill based alignment method was considered a better method according to different validation parameters. A 3D-QSAR model was generated and contour maps were discussed. The biological activity of designed molecules were predicted using generated QSAR model to validate QSAR. The newly designed molecules were docked to predict binding affinity. Results: In CoMFA, leave one out cross validated coefficient (q 2 ), conventional coefficient (r 2 ) and predicted correlation coefficient (r 2 Predicted) values were found to be 0.721, 0.991 and 0.780, respectively. The best obtained CoMSIA model also had significant cross validated coefficient (q 2 ), conventional coefficient (r 2 ) and predicted correlation coefficient (r 2 Predicted) values of 0.714, 0.987 and 0.721, respectively. Steric and electrostatic contour maps generated from CoMFA and hydrophobic and hydrogen bond donor and hydrogen bond acceptor contour maps from CoMSIA models were used to design new and bioactive protease inhibitors by incorporating bioisosterism and knowledge based structure activity relationship. Conclusion: The results from both these approaches, ligand based drug design and structure based drug design, are adequate and promising to discover protease inhibitors.

scholarly journals Prediction of Novel Anoctamin1 (ANO1) Inhibitors Using 3D-QSAR Pharmacophore Modeling and Molecular Docking

2018 ◽  
Vol 19 (10) ◽  
pp. 3204 ◽  
Author(s):  
Yoon Lee ◽  
Gwan-Su Yi
Keyword(s):  
Hydrogen Bond ◽  
Molecular Docking ◽  
Correlation Coefficient ◽  
High Throughput Screening ◽  
Interaction Analysis ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Hydrogen Bond Acceptor ◽  
Test Set

Recently, anoctamin1 (ANO1), a calcium-activated chloride channel, has been considered an important drug target, due to its involvement in various physiological functions, as well as its possibility for treatment of cancer, pain, diarrhea, hypertension, and asthma. Although several ANO1 inhibitors have been discovered by high-throughput screening, a discovery of new ANO1 inhibitors is still in the early phase, in terms of their potency and specificity. Moreover, there is no computational model to be able to identify a novel lead candidate of ANO1 inhibitor. Therefore, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore modeling approach was employed for identifying the essential chemical features to be required in the inhibition of ANO1. The pharmacophore hypothesis 2 (Hypo2) was selected as the best model based on the highest correlation coefficient of prediction on the test set (0.909). Hypo2 comprised a hydrogen bond acceptor, a hydrogen bond donor, a hydrophobic, and a ring aromatic feature with good statistics of the total cost (73.604), the correlation coefficient of the training set (0.969), and the root-mean-square deviation (RMSD) value (0.946). Hypo2 was well assessed by the test set, Fischer randomization, and leave-one-out methods. Virtual screening of the ZINC database with Hypo2 retrieved the 580 drug-like candidates with good potency and ADMET properties. Finally, two compounds were selected as novel lead candidates of ANO1 inhibitor, based on the molecular docking score and the interaction analysis. In this study, the best pharmacophore model, Hypo2, with notable predictive ability was successfully generated, and two potential leads of ANO1 inhibitors were identified. We believe that these compounds and the 3D-QSAR pharmacophore model could contribute to discovering novel and potent ANO1 inhibitors in the future.

Identification of new checkpoint kinase-1 (Chk1) inhibitors by docking, 3D-QSAR, and pharmacophore-modeling methods

2012 ◽  
Vol 90 (8) ◽  
pp. 675-692 ◽  
Author(s):  
Premlata K. Ambre ◽  
Raghuvir R. S. Pissurlenkar ◽  
Evans C. Coutinho ◽  
Radhakrishnan P. Iyer
Keyword(s):  
Hydrogen Bond ◽  
3D Qsar ◽  
Pharmacophore Modeling ◽  
Docking Studies ◽  
Hydrogen Bond Donor ◽  
Hydrogen Bond Acceptor ◽  
Checkpoint Kinase ◽  
Checkpoint Kinase 1 ◽  
Qsar Models ◽  
Potential Inhibitors

Inhibition of checkpoint kinase-1 (Chk1) by small molecules is of great therapeutic interest in the field of oncology and for understanding cell-cycle regulations. This paper presents a model with elements from docking, pharmacophore mapping, the 3D-QSAR approaches CoMFA, CoMSIA and CoRIA, and virtual screening to identify novel hits against Chk1. Docking, 3D-QSAR (CoRIA, CoMFA and CoMSIA), and pharmacophore studies delineate crucial site points on the Chk1 inhibitors, which can be modified to improve activity. The docking analysis showed residues in the proximity of the ligands that are involved in ligand–receptor interactions, whereas CoRIA models were able to derive the magnitude of these interactions that impact the activity. The ligand-based 3D-QSAR methods (CoMFA and CoMSIA) highlight key areas on the molecules that are beneficial and (or) detrimental for activity. The docking studies and 3D-QSAR models are in excellent agreement in terms of binding-site interactions. The pharmacophore hypotheses validated using sensitivity, selectivity, and specificity parameters is a four-point model, characterized by a hydrogen-bond acceptor (A), hydrogen-bond donor (D), and two hydrophobes (H). This map was used to screen a database of 2.7 million druglike compounds, which were pruned to a small set of potential inhibitors by CoRIA, CoMFA, and CoMSIA models with predicted activity in the range of 8.5–10.5 log units.

scholarly journals Structural investigations of CXCR2 receptor antagonists by CoMFA, CoMSIA and flexible docking studies

2012 ◽  
Vol 62 (3) ◽  
pp. 287-304 ◽  
Author(s):  
Shravan Kumar Gunda ◽  
Rohith Kumar Anugolu ◽  
Sri Ramya Tata ◽  
Saikh Mahmood
Keyword(s):  
Standard Error ◽  
Three Dimensional ◽  
3D Qsar ◽  
Homology Model ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Structural Investigations ◽  
Qsar Analysis ◽  
Qsar Studies

= Three-dimensional quantitative structure activity relationship (3D QSAR) analysis was carried out on a et of 56 N,N’-diarylsquaramides, N,N’-diarylureas and diaminocyclobutenediones in order to understand their antagonistic activities against CXCR2. The studies included comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Models with good predictive abilities were generated with CoMFA q2 0.709, r2 (non-cross-validated square of correlation coefficient) = 0.951, F value = 139.903, r2 bs = 0.978 with five components, standard error of estimate = 0.144 and the CoMSIA q2 = 0.592, r2 = 0.955, F value = 122.399, r2 bs = 0.973 with six components, standard error of estimate = 0.141. In addition, a homology model of CXCR2 was used for docking based alignment of the compounds. The most active compound then served as a template for alignment of the remaining structures. Further, mapping of contours onto the active site validated each other in terms of residues involved with reference to the respective contours. This integrated molecular docking based alignment followed by 3D QSAR studies provided a further insight to support the structure-based design of CXCR2 antagonistic agents with improved activity profiles. Furthermore, in silico screening was adapted to the QSAR model in order to predict the structures of new, potentially active compounds.

scholarly journals Integrated Ligand and Structure-Based Investigation of Structural Requirements for Silent Information Regulator 1 (SIRT1) Activation

2019 ◽  
Vol 18 (27) ◽  
pp. 2313-2324
Author(s):  
Amit K. Gupta ◽  
Sun Choi
Keyword(s):  
3D Model ◽  
Therapeutic Agent ◽  
Statistical Significance ◽  
3D Qsar ◽  
Qsar Model ◽  
Hydrogen Bond Acceptor ◽  
High Statistical Significance ◽  
Qsar Models ◽  
Silent Information Regulator 1 ◽  
2D And 3D

A series of imidazothiazole and oxazolopyridine derivatives as human Silent Information Regulator 1 (SIRT1) activators were subjected to the integrated 2D and 3D QSAR approaches. The derived 3D QSAR models yielded high cross-validated q2 values of 0.682 and 0.628 for CoMFA and CoMSIA, respectively. The non-cross validated values of r2 training = 0.89; predictive r2 test = 0.69 for CoMFA and r2=0.87; predictive r2 test =0.67 for CoMSIA reflected the statistical significance of the developed model. The steric, electrostatic, hydrophobic and hydrogen bond acceptor interactions have been found important in describing the variation in human SIRT1 activation. Further, 2D QSAR model for the same dataset yielded high statistical significance and derived 2D model’s parameters corroborated with the 3D model in terms of features. Derived model was also validated by the crystal structure of active conformation of SIRT1. Developed models may be useful for the identification of potential novel human SIRT1 activators as a therapeutic agent.

scholarly journals Kinase Inhibitors Can Inhibit SARS-CoV-2 Mpro: A Theoretical Study

2020 ◽  
Author(s):  
BN Acharya
Keyword(s):  
Kinase Inhibitors ◽  
Theoretical Study ◽  
3D Qsar ◽  
Qsar Model ◽  
Randomization Test ◽  
Pharmacophore Modeling ◽  
Ligand Docking ◽  
Receptor Ligand ◽  
Test Set ◽  
Approved Drugs

This study describes screening of DrugBank library for approved drugs by pharmacophore modeling and receptor-ligand docking. A 3D-QSAR model was generated on the<br>inhibition constants (Ki AutoDock ) determined by AutoDock. This 3D-QSAR model was statistically validated by Fischer’s randomization test and further evaluated by a test set<br>comprising 75 molecules. Ki AutoDock values of 49 molecules were predicted correctly by the 3D-QSAR model. The validated 3D-QSAR model was used for screening of DrugBank library for approved molecules to identify potential molecules against novel SARS corona virus-2 (SARS-CoV-2). Ten out of 40 the shortlisted molecules were kinase inhibitors.

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

INDIAN DRUGS ◽  
2019 ◽  
Vol 56 (12) ◽  
pp. 62-67
Author(s):  
M. C Sharma ◽  
◽  
D. V. Kohli
Keyword(s):  
Nearest Neighbor ◽  
Three Dimensional ◽  
Antihypertensive Agents ◽  
3D Qsar ◽  
Good Alternative ◽  
Qsar Model ◽  
Field Analysis ◽  
K Nearest Neighbor ◽  
Qsar Modeling ◽  
Qsar Studies

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.

SYNTHESIS, ANTIMALARIAL ACTIVITY AND QSAR STUDIES OF CHALCONES,N-BENZYLIDENESULFONAMIDES AND CHALCONESULFONAMIDES

INDIAN DRUGS ◽  
2012 ◽  
Vol 49 (05) ◽  
pp. 20-34
Author(s):  
A. H. More ◽  
◽  
S. J Raul ◽  
S. S Mahajan
Keyword(s):  
Antimalarial Activity ◽  
Qsar Model ◽  
The Other ◽  
Acute Oral Toxicity ◽  
Microwave Irradiation Method ◽  
Oral Toxicity ◽  
Training Set ◽  
Test Set ◽  
Qsar Studies ◽  
Life Threatening

Malaria remains the major cause of human morbidity and mortality worldwide. Malaria, caused byPlasmodium species, is potentially life threatening, increasing in prevalence and becoming evenmore resistant to in-use drugs. In this article, synthesis of compounds from the series of chalcones,benzylidenesulfonamides and chalconesulfonamides, by the conventional and microwave-irradiationmethods is discussed. The microwave-irradiation method was convenient, rapid and high yielding ascompared to the conventional method of synthesis. The acute oral toxicity studies indicated that all thecompounds were safe for administration up to 2000 mg/kg body weight of a mouse. The compoundswere screened for their antimalarial activity. Two chalcones, five benzylidenesulfonamides and threechalconesulfonamides showed antimalarial activity equivalent to chloroquine. Benzylidenesulfonamidesshowed better antimalarial activity compared to the compounds from the other two series.Chalconesulfonamides showed better antimalarial activity than chalcones. The QSAR studies werecarried out by correlating antimalarial activity of all the compounds with their physicochemical descriptors.Validation of the best QSAR model was carried out using the training set and the test set method. Thesestudies provided guidance for the development of novel antimalarials from these series.

scholarly journals VIRTUAL SCREENING, PHARMACOPHORE MODELING, AND QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP STUDIES ON HISTAMINE 4 RECEPTOR

2017 ◽  
Vol 10 (12) ◽  
pp. 150
Author(s):  
Shobana Sugumar
Keyword(s):  
Virtual Screening ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Pharmacophore Modeling ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Hydrogen Bond Acceptor ◽  
Structure Activity

  Objective: To find out novel inhibitors for histamine 4 receptor (H4R), the target for various allergic and inflammatory pathophysiological conditions.Methods: Homology modeling of H4R was performed using easy modeler and validated using structure analysis and verification server, and with the modeled structure, virtual screening, pharmacophore modeling, and quantitative structure activity relationship (QSAR) studies were performed using the Schrodinger 9.3 software.Results: Among all the synthetic and natural ligands, hesperidin, vitexin, and diosmin were found to have the highest dock score, and with that, a five-point pharmacophore model was developed consisting of two hydrogen bond acceptor and three ring atoms, and the pharmacophore hypothesis yielded a statistically significant three-dimensional QSAR (3D-QSAR) model with a correlation coefficient of r2=0.8962 as well as good predictive power.Conclusion: The pharmacophore-based 3D-QSAR model generated from natural antihistamines can provide intricate structural knowledge about a new class of anti-allergic and anti-inflammatory drug research.

scholarly journals Chemical Feature-Based Molecular Modeling of Urotensin-II Receptor Antagonists: Generation of Predictive Pharmacophore Model for Early Drug Discovery

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Anubhuti Pandey ◽  
Sarvesh Kumar Paliwal ◽  
Shailendra Kumar Paliwal
Keyword(s):  
Predictive Power ◽  
Pharmacophore Model ◽  
Urotensin Ii ◽  
Hydrogen Bond Acceptor ◽  
Test Set ◽  
Lead Compounds ◽  
Mapping Analysis ◽  
Feature Based ◽  
The Cost ◽  
Early Drug

For a series of 35 piperazino-phthalimide and piperazino-isoindolinone based urotensin-II receptor (UT) antagonists, a thoroughly validated 3D pharmacophore model has been developed, consisting of four chemical features: one hydrogen bond acceptor lipid (HBA_L), one hydrophobe (HY), and two ring aromatic (RA). Multiple validation techniques like CatScramble, test set prediction, and mapping analysis of advanced known antagonists have been employed to check the predictive power and robustness of the developed model. The results demonstrate that the best model, Hypo 1, shows a correlation (r) of 0.902, a root mean square deviation (RMSD) of 0.886, and the cost difference of 39.69 bits. The model obtained is highly predictive with good correlation values for both internal (r2=0.707) as well as external (r2=0.614) test set compounds. Moreover, the pharmacophore model has been used as a 3D query for virtual screening which served to detect prospective new lead compounds which can be further optimized as UT antagonists with potential for treatment of cardiovascular diseases.

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