scholarly journals 3D-QSAR Studies of 1,2,4-Oxadiazole Derivatives as Sortase A Inhibitors

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Neda Shakour ◽  
Farzin Hadizadeh ◽  
Prashant Kesharwani ◽  
Amirhossein Sahebkar
Keyword(s):  
Test Group ◽  
3D Qsar ◽  
Training Group ◽  
Data Bank ◽  
Qsar Model ◽  
Randomization Test ◽  
Sortase A ◽  
Bacterial Strains ◽  
Qsar Studies ◽  
Oxadiazole Derivatives

Sortase A (SrtA) is an enzyme that catalyzes the attachment of proteins to the cell wall of Gram-positive bacterial membrane, preventing the spread of pathogenic bacterial strains. Here, one class of oxadiazole compounds was distinguished as an efficient inhibitor of SrtA via the “S. aureus Sortase A” substrate-based virtual screening. The current study on 3D-QSAR was done by utilizing preparation of the structure in the Schrödinger software suite and an assessment of 120 derivatives with the crystal structure of 1,2,4-oxadiazole which was extracted from the PDB data bank. The docking operation of the best compound in terms of pMIC ( pMIC = 2.77 ) was done to determine the drug likeliness and binding form of 1,2,4-oxadiazole derivatives as antibiotics in the active site. Using the kNN-MFA way, seven models of 3D-QSAR were created and amongst them, and one model was selected as the best. The chosen model based on q 2 (pred_ r 2 ) and R 2 values related to the sixth factor of PLS illustrates better and more acceptable external and internal predictions. Values of crossvalidation (pred_ r 2 ), validation ( q 2 ), and F were observed 0.5479, 0.6319, and 179.0, respectively, for a test group including 24 molecules and the training group including 96 molecules. The external reliability outcomes showed that the acceptable and the selective 3D-QSAR model had a high predictive potential ( R 2 = 0.9235 ) which was confirmed by the Y -randomization test. Besides, the model applicability domain was described successfully to validate the estimation of the model.

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 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.

Molecular Docking and 3D-QSAR Studies of 4-Phenylpiperidine Derivatives as μ-Opioid Agonists

2011 ◽  
Vol 361-363 ◽  
pp. 263-267 ◽  
Author(s):  
Ming Liu ◽  
Wen Xiang Hu ◽  
Xiao Li Liu
Keyword(s):  
Electrostatic Interactions ◽  
Biological Activities ◽  
3D Qsar ◽  
Qsar Model ◽  
Field Analysis ◽  
Qsar Studies ◽  
Chemical Structures ◽  
Opioid Agonists ◽  
Qsar Models ◽  
Leave One Out

A predictive 3D-QSAR model which correlates the biological activities with the chemical structures of a series of 4-phenylpiperidine derivatives as μ opioid agonists was developed by means of comparative molecular field analysis (CoMFA). The stabilities of the 3D-QSAR models were verified by the leave-one-out cross-validation method. Moreover, the predictive capabilities of the models were validated by an external test set. Best predictions were obtained with CoMFA standard model(q2=0.504, N=6, r2=0.968) which revealed how steric and electrostatic interactions contribute to agonists bioactivities, and provided us with important information to understand the interaction of agonists and μ opioid receptor .

scholarly journals Docking and 3D QSAR Studies on p38α MAP Kinase Inhibitors

2011 ◽  
Vol 8 (4) ◽  
pp. 1596-1605
Author(s):  
Mohan Babu Jatavath ◽  
Sree Kanth Sivan ◽  
Yamini Lingala ◽  
Vijjulatha Manga
Keyword(s):  
Map Kinase ◽  
Kinase Inhibitors ◽  
Inflammatory Diseases ◽  
Biological Activities ◽  
Three Dimensional ◽  
3D Qsar ◽  
Qsar Model ◽  
Chemotherapeutic Agents ◽  
Field Analysis ◽  
Qsar Studies

The p38 signaling cascade has emerged as an attractive target for the design of novel chemotherapeutic agents for the treatment of inflammatory diseases. Three dimensional quantitative structure- activity relationship (3D- QSAR) studies were performed on a series of 25, 2-aminothiazole analogs as inhibitors of p38α mitogen activated protein (MAP) kinase. The docking results provided a reliable conformational alignment scheme for the 3D-QSAR model. The 3D-QSAR model showed very good statistical results namely q2, r2and r2predvalues for both comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The CoMFA and CoMSIA models & docking results provided the most significant correlation of steric, electrostatic, hydrophobic,H-bond donor,H-bond acceptor fields with biological activities and the provided values were in good agreement with the experimental results. The information rendered from molecular modeling studies gave valuable clues to optimize the lead and design new potential inhibitors.

3D QSAR Based Virtual Screening of Pyrido[1,2-a] Benzimidazoles as Potent Antimalarial Agents

2019 ◽  
Vol 16 (3) ◽  
pp. 301-312
Author(s):  
Kalicharan Sharma ◽  
Apeksha Srivastava ◽  
Pooja Tiwari ◽  
Shweta Sharma ◽  
Mohammad Shaquiquzzaman ◽  
...  
Keyword(s):  
Virtual Screening ◽  
Antimalarial Activity ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Qsar Model ◽  
Docking Studies ◽  
Activity Data ◽  
Qsar Studies ◽  
Antimalarial Agents ◽  
Fisher Ratio

Background: Development of novel antimalarial agents has been one of the sought areas in medicinal chemistry. In this study the same was done by virtual screening of in-house database on developed QSAR model. </P><P> Methods: A six point pharmacophore model was generated (AADHRR.56) from 41 compounds using PHASE module of Schrodinger software and used for pharmacophore based search. Docking studies of the obtained hits were performed using GLIDE. Most promising hit was synthesized & biologically evaluated for antimalarial activity. </P><P> Result: The best generated model was found to be statistically significant as it had a high correlation coefficient r2= 0.989 and q2 =0.76 at 3 component PLS factor. The significance of hypothesis was also confirmed by high Fisher ratio (F = 675.1) and RMSE of 0.2745. The model developed had good predicted coefficient (Pearson R = 0.8826). The virtual screening on this model resulted in six hits, which were docked against FP-2 enzyme. The synthesized compound displayed IC50 value of 0.27&#181;g/ml against CQS (3D7) and 0.57μg/ml against CQR (RKL9). </P><P> Conclusion: 3D QSAR studies reviled that hydrophobic groups are important for anti-malarial activity while H-donor is less desirable for the same. Electron withdrawing groups at R1 position favours the activity. The biological activity data of the synthesized hit proved that the pharmacophore hypothesis developed could be utilized for developing novel anti-malarial drugs.

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.

Synthesis of 1,2,4-oxadiazole derivatives: anticancer and 3D QSAR studies

2020 ◽  
Vol 151 (3) ◽  
pp. 385-395 ◽  
Author(s):  
Ankur Vaidya ◽  
Shweta Jain ◽  
Br Prashantha Kumar ◽  
Shashank K. Singh ◽  
Sushil Kumar Kashaw ◽  
...  
Keyword(s):  
3D Qsar ◽  
Qsar Studies ◽  
Oxadiazole Derivatives

scholarly journals Design and Synthesis of a Novel PLK1 Inhibitor Scaffold Using a Hybridized 3D-QSAR Model

2021 ◽  
Vol 22 (8) ◽  
pp. 3865
Author(s):  
Youri Oh ◽  
Hoyong Jung ◽  
Hyejin Kim ◽  
Jihyun Baek ◽  
Joonhong Jun ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Cell Cycle Progression ◽  
3D Qsar ◽  
Qsar Model ◽  
Cycle Progression ◽  
Qsar Studies ◽  
Design And Synthesis ◽  
Qsar Models ◽  
Inhibitory Activities ◽  
Anticancer Drug Resistance

Polo-like kinase 1 (PLK1) plays an important role in cell cycle progression and proliferation in cancer cells. PLK1 also contributes to anticancer drug resistance and is a valuable target in anticancer therapeutics. To identify additional effective PLK1 inhibitors, we performed QSAR studies of two series of known PLK1 inhibitors and proposed a new structure based on a hybridized 3D-QSAR model. Given the hybridized 3D-QSAR models, we designed and synthesized 4-benzyloxy-1-(2-arylaminopyridin-4-yl)-1H-pyrazole-3-carboxamides, and we inspected its inhibitory activities to identify novel PLK1 inhibitors with decent potency and selectivity.

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.

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