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

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.

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CoMFA and CoMSIA Studies on Inhibitors of HIV-1 Integrase - Bicyclic Pyrimidinones

E-Journal of Chemistry ◽

10.1155/2010/717865 ◽

2010 ◽

Vol 7 (s1) ◽

pp. S75-S84 ◽

Cited By ~ 1

Author(s):

V. Radhika ◽

S. Sree Kanth ◽

M. Vijjulatha

Keyword(s):

Biological Activities ◽

3D Qsar ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Field Analysis ◽

Integrase Inhibitors ◽

Inhibitory Potency ◽

Similarity Indices ◽

Qsar Models ◽

Hiv 1

To understand the structural requirements of HIV-1 integrase inhibitors and to design new ligands against human HIV-1 integrase with enhanced inhibitory potency, a 3D QSAR (quantitative structure-activity relationship) study with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for a dataset of 35 bicyclic pyrimidinones which are inhibitors of human HIV-1 integrase was performed. QSAR models were computed with Sybyl. The 3D QSAR model showed very good statistical result, namely q2, r2and r2predvalues were high for both CoMFA and CoMSIA. Based on the high values for q2and r2we are confident that the 3D QSAR model gives good predictions that may be used to design better HIV-1 integrase inhibitors. The CoMFA and CoMSIA models reveal that steric and electrostatic fields contribute significantly with biological activities of the studied compounds.

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Antiaging, antioxidant flavonoids; synthesis, antimicrobial screening as well as 3D QSAR CoMFA models for the prediction of biological activity

Journal of the Asiatic Society of Bangladesh Science ◽

10.3329/jasbs.v39i2.17856 ◽

2014 ◽

Vol 39 (2) ◽

pp. 191-199 ◽

Cited By ~ 4

Author(s):

Bilkis Jahan Lumbiny ◽

Zhang Hui ◽

M Azizul Islam

Keyword(s):

Predictive Model ◽

Biological Activities ◽

3D Qsar ◽

Biologically Active ◽

Field Analysis ◽

Contour Maps ◽

Chemical Structures ◽

Hydroxy Flavone ◽

Bonding Characteristics ◽

Better Than

Flavonoids, polyphenolic heteronuclear compounds which are naturally occurring antioxidants are widely used as antiaging substances. Synthesis of new naturally occuring organic compounds with basic skeleton of chalcones, flavones and oxygenated flavones and their antimicrobial activity were reported by this research group for long. Presently comparative molecular field analysis (CoMFA) implemented in Sybyl 7.3 was conducted on a series of substituted flavones. CoMFA is an effective computer implemented 3D QSAR technique deriving a correlation between set of the biologically active molecules and their 3D shape, electrostatic and hydrogen bonding characteristics employing both interactive graphics and statistical techniques. Evaluation of 38 compounds were served to establish the models with grid spacing (2.0 Å). CoMFA produced best predictive model for compound 1C (2 ? Phenyl ? 1,4 ? benzopyrone) and compound 2C (5 ? Fluoro ? 3?? hydroxy flavone ) among all. Model for compound 2C [r2 conv (no-validation) = 0.956, SEE = 0.211, F value = 111.054) is better than that of compound 1C [r2 conv (no-validation) = 0.955, SEE = 0.212, F value = 110.261) but comparing superimposed model 1C being suggested as the best predictive model. 3D contour maps were generated to correlate the biological activities with the chemical structures of the examined compounds and for further design. DOI: http://dx.doi.org/10.3329/jasbs.v39i2.17856 J. Asiat. Soc. Bangladesh, Sci. 39(2): 191-199, December 2013

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2D AND 3D-QSAR ANALYSIS OF AMINO (3-((3, 5-DIFLUORO-4-METHYL-6-PHENOXYPYRIDINE-2-YL) OXY) PHENYL) METHANIMINIUM DERIVATIVES AS FACTOR XA INHIBITOR

International Journal of Pharmacy and Pharmaceutical Sciences ◽

10.22159/ijpps.2019v11i2.21067 ◽

2019 ◽

pp. 104-114

Author(s):

Smita Suhane ◽

A. G. Nerkar ◽

Kumud Modi ◽

Sanjay D. Sawant

Keyword(s):

Factor Xa ◽

3D Qsar ◽

Qsar Model ◽

Regression Method ◽

Field Analysis ◽

Factor Xa Inhibitors ◽

Nearest Neighbour ◽

Qsar Analysis ◽

Qsar Models ◽

2D And 3D

Objective: The main objective of the present study was to evolve a novel pharmacophore of methaniminium derivatives as factor Xa inhibitors by developing best 2D and 3D QSAR models. The models were developed for amino (3-((3, 5-difluoro-4-methyl-6-phenoxypyridine-2-yl) oxy) phenyl) methaniminium derivatives as factor Xa inhibitors. Methods: With the help of Marvin application, 2D structures of thirty compounds of methaniminium derivatives were drawn and consequently converted to 3D structures. 2D QSAR using multiple linear regression (MLR) analysis and PLS regression method was performed with the help of molecular design suite VLife MDS 4.3.3. 3D QSAR analysis was carried out using k-Nearest Neighbour Molecular Field Analysis (k-NN-MFA). Results: The most significant 2D models of methaniminium derivatives calculated squared correlation coefficient value 0.8002 using multiple linear regression (MLR) analysis. Partial Least Square (PLS) regression method was also employed. The results of both the methods were compared. In 2D QSAR model, T_C_O_5, T_2_O_2, s log p, T_2_O_1 and T_2_O_6 descriptors were found significant. The best 3D QSAR model with k-Nearest Neighbour Molecular Field Analysis have predicted q2 value 0.8790, q2_se value 0.0794, pred r2 value 0.9340 and pred_r2 se value 0.0540. The stepwise regression method was employed for anticipating the inhibitory activity of this class of compound. The 3D model demonstrated that hydrophobic, electrostatic and steric descriptors exhibit a crucial role in determining the inhibitory activity of this class of compounds. Conclusion: The developed 2D and 3D QSAR models have shown good r2 and q2 values of 0.8002 and 0.8790 respectively. There is high agreement in inhibitory properties of experimental and predicted values, which suggests that derived QSAR models have good predicting properties. The contour plots of 3D QSAR (k-NN-MFA) method furnish additional information on the relationship between the structure of the compound and their inhibitory activities which can be employed to construct newer potent factor Xa inhibitors.

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2D and 3D QSAR Studies on a Series of Antichagasic Fenarimol Derivatives

International Journal of Quantitative Structure-Property Relationships ◽

10.4018/ijqspr.2017010104 ◽

2017 ◽

Vol 2 (1) ◽

pp. 44-63

Author(s):

Anacleto S. de Souza ◽

Leonardo G. Ferreira ◽

Adriano D. Andricopulo

Keyword(s):

Neglected Tropical Diseases ◽

Public Health Problem ◽

Predictive Ability ◽

3D Qsar ◽

Structural Features ◽

Field Analysis ◽

Global Public Health ◽

Qsar Studies ◽

Qsar Models ◽

2D And 3D

Chagas disease is one of the most important neglected tropical diseases. Endemic in Latin America, the disease is a global public health problem, affecting several countries in North America, Europe, Asia and Oceania. The disease affects around 8-10 million people worldwide and the limited treatments available present low efficacy and severe side effects, highlighting the urgent need for new therapeutic options. In this work, the authors developed QSAR models for a series of fenarimol derivatives exhibiting anti-T. cruzi activity. The models were constructed using the Hologram QSAR (HQSAR), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. The QSAR models presented substantial predictive ability for a series of test set compounds (HQSAR, r2pred = 0.66; CoMFA, r2pred = 0.82; and CoMSIA, r2pred = 0.76), and were valuable to identify key structural features related to the observed trypanocidal activity. The results reported herein are useful for the design of novel derivatives having improved antichagasic properties.

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

Acta Pharmaceutica ◽

10.2478/v10007-012-0029-7 ◽

2012 ◽

Vol 62 (3) ◽

pp. 287-304 ◽

Cited By ~ 1

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.

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2D and 3D QSAR Studies on a Series of Antichagasic Fenarimol Derivatives

Pharmaceutical Sciences ◽

10.4018/978-1-5225-1762-7.ch037 ◽

2017 ◽

pp. 956-977

Author(s):

Anacleto S. de Souza ◽

Leonardo G. Ferreira ◽

Adriano D. Andricopulo

Keyword(s):

Neglected Tropical Diseases ◽

Public Health Problem ◽

Predictive Ability ◽

3D Qsar ◽

Structural Features ◽

Field Analysis ◽

Global Public Health ◽

Qsar Studies ◽

Qsar Models ◽

2D And 3D

Chagas disease is one of the most important neglected tropical diseases. Endemic in Latin America, the disease is a global public health problem, affecting several countries in North America, Europe, Asia and Oceania. The disease affects around 8-10 million people worldwide and the limited treatments available present low efficacy and severe side effects, highlighting the urgent need for new therapeutic options. In this work, the authors developed QSAR models for a series of fenarimol derivatives exhibiting anti-T. cruzi activity. The models were constructed using the Hologram QSAR (HQSAR), Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods. The QSAR models presented substantial predictive ability for a series of test set compounds (HQSAR, r2pred = 0.66; CoMFA, r2pred = 0.82; and CoMSIA, r2pred = 0.76), and were valuable to identify key structural features related to the observed trypanocidal activity. The results reported herein are useful for the design of novel derivatives having improved antichagasic properties.

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

INDIAN DRUGS ◽

10.53879/id.56.12.11234 ◽

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.

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Structure Activity Relationships, QSAR Modeling and Drug-Like Calculations of TP Inhibition of 1,3,4-Oxadiazoline-2-Thione Derivatives

International Letters of Chemistry Physics and Astronomy ◽

10.18052/www.scipress.com/ilcpa.37.113 ◽

2014 ◽

Vol 37 ◽

pp. 113-124 ◽

Cited By ~ 8

Author(s):

Zineb Almi ◽

Salah Belaidi ◽

Touhami Lanez ◽

Noureddine Tchouar

Keyword(s):

Predictive Power ◽

Cross Validation ◽

Molecular Descriptor ◽

Qsar Model ◽

Qsar Modeling ◽

Qsar Studies ◽

Structure Activity ◽

Qsar Models ◽

Leave One Out

QSAR studies have been performed on twenty-one molecules of 1,3,4-oxadiazoline-2-thiones. The compounds used are among the most thymidine phosphorylase (TP) inhibitors. A multiple linear regression (MLR) procedure was used to design the relationships between molecular descriptor and TP inhibition of the 1,3,4-oxadiazoline-2-thione derivatives. The predictivity of the model was estimated by cross-validation with the leave-one-out method. Our results suggest a QSAR model based of the following descriptors: logP, HE, Pol, MR, MV, and MW, qO1, SAG, for the TP inhibitory activity. To confirm the predictive power of the models, an external set of molecules was used. High correlation between experimental and predicted activity values was observed, indicating the validation and the good quality of the derived QSAR models.

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Biological enrichment prediction of polychlorinated biphenyls and novel molecular design based on 3D-QSAR/HQSAR associated with molecule docking

Bioscience Reports ◽

10.1042/bsr20180409 ◽

2019 ◽

Vol 39 (5) ◽

Cited By ~ 1

Author(s):

Jiawen Yang ◽

Wenwen Gu ◽

Yu Li

Keyword(s):

Partition Coefficients ◽

Polychlorinated Biphenyl ◽

Molecular Design ◽

3D Qsar ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Field Analysis ◽

Similarity Indices ◽

Qsar Models ◽

Molecule Docking

Abstract Based on the experimental data of octanol-water partition coefficients (Kow, represents bioaccumulation) for 13 polychlorinated biphenyl (PCB) congeners, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to establish 3D-QSAR models, combined with the hologram quantitative structure–activity relationship (HQSAR), the substitution sites (mono-substituted and bis-substituted) and substituent groups (electron-withdrawing hydrophobic groups) that significantly affect the octanol-water partition coefficients values of PCBs were identified, a total of 63 monosubstituted and bis-substituted were identified. Compared with using 3D-QSAR model alone, the coupling of 3D-QSAR and HQSAR models greatly increased the number of newly designed bis-substituted molecules, and the logKow reduction in newly designed bis-substituted molecules was larger than that of monosubstituted molecules. This was established to predict the Kow values of 196 additional PCBs and carry out a modification of target molecular PCB-207 to lower its Kow (biological enrichment) significantly, simultaneously maintaining the flame retardancy and insulativity after calculation by using Gaussian09. Simultaneously, molecular docking could further screen out three more environmental friendly low biological enrichment newly designed PCB-207 molecules (5-methyl-PCB-207, 5-amino-PCB-207, and 4-amino-5-ethyl-PCB-207).

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