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

2019 ◽  
Vol 39 (5) ◽  
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).

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

2010 ◽  
Vol 7 (s1) ◽  
pp. S75-S84 ◽  
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.

scholarly journals Receptor Guided 3D-QSAR: A Useful Approach for Designing of IGF-1R Inhibitors

2008 ◽  
Vol 2008 ◽  
pp. 1-9 ◽  
Author(s):  
M. Muddassar ◽  
F. A. Pasha ◽  
H. W. Chung ◽  
K. H. Yoo ◽  
C. H. Oh ◽  
...  
Keyword(s):  
Structural Information ◽  
Three Dimensional ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Comparative Molecular Field Analysis ◽  
Qsar Studies ◽  
Similarity Indices ◽  
Qsar Models ◽  
Derivatives Of

Research by other investigators has established that insulin-like growth factor‐1 receptor (IGF-1R) is a key oncological target, and that derivatives of 1, 3-disubstituted-imidazo[1,5-] pyrazine are potent IGF-1R inhibitors. In this paper, we report on our three-dimensional quantitative structure activity relationship (3D-QSAR) studies for this series of compounds. We validated the 3D-QSAR models by the comparison of two major alignment schemes, namely, ligand-based (LB) and receptor-guided (RG) alignment schemes. The latter scheme yielded better 3D-QSAR models for both comparative molecular field analysis (CoMFA) (, ) and comparative molecular similarity indices analysis (CoMSIA) (, ). We submit that this might arise from the more accurate inhibitor alignment that results from using the structural information of the active site. We conclude that the receptor-guided 3D-QSAR may be helpful to design more potent IGF-1R inhibitors, as well as to understand their binding affinity with the receptor.

scholarly journals 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

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.

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.

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 .

COMPARISON OF THREE 3D-QSAR METHODS USING A NOVEL CLASS OF MURF INHIBITORS

2007 ◽  
Vol 06 (01) ◽  
pp. 63-80 ◽  
Author(s):  
DE-XIN KONG ◽  
WEI-LIANG ZHU ◽  
DA-LEI WU ◽  
XU SHEN ◽  
HUA-LIANG JIANG
Keyword(s):  
Cross Validation ◽  
Molecular Similarity ◽  
Predictive Ability ◽  
3D Qsar ◽  
Field Analysis ◽  
Comparative Molecular Field Analysis ◽  
Virtual Database ◽  
Similarity Indices ◽  
Qsar Models ◽  
Good Agreement

MurF was considered as an attractive target for new antibacterial discovery. In this paper, three QSAR methods were employed, viz. comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and hologram QSAR (HQSAR), to derive highly predictive QSAR models for designing novel MurF inhibitors and comparing different 3D-QSAR/alignment methods. QSAR models with high predictive ability for MurF inhibitors were successfully constructed in terms of cross-validation q2, standard error and predictive coefficient r2, which were around 0.70, 0.55 and 0.99, respectively. All the models from different methods were in good agreement with each other. Compounds with indeterminate activities were used as a test set; results showed that CoMSIA had the best predictive ability, followed by HQSAR and CoMFA. Based on these models, some key features for designing new MurF inhibitors were identified. A virtual database screen process was proposed based on the combination of these models.

scholarly journals QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS OF NOVEL PYRAZOLINE DERIVATIVES USING K NEAREST NEIGHBOUR MOLECULAR FIELD ANALYSIS METHOD

2017 ◽  
Vol 9 (12) ◽  
pp. 87
Author(s):  
Deepali M. Jagdale ◽  
Ramaa C. S.
Keyword(s):  
Three Dimensional ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Quantitative Structure ◽  
Relationship Analysis ◽  
Structure Activity ◽  
Novel Approach ◽  
Vlife Mds

Objective: Malonyl CoA decarboxylase (MCD) enzyme plays important role in fatty acid and glucose oxidation. Inhibition of MCD might turn to a novel approach to treat ischemia. The main objective of this research article was to develop a novel pharmacophore for enhanced activity.Methods: Three-dimensional quantitative structure-activity relationships (3D-QSAR) was performed for pyrazoline derivatives as MCD inhibitors using VLife MDS 4.6 software. The QSAR model was developed using the stepwise 3D-QSAR kNN-MFA method.Results: The statistical results generated from kNN-MFA method indicated the significance and requirements for better MCD inhibitory activity. The information rendered by 3D-QSAR model may render to better understanding and designing of novel MCD inhibitors.Conclusion: 3D-QSAR is an important tool in understanding the structural requirements for the design of novel and potent MCD inhibitors. It can be employed to design new drug discovery.

3D-QSAR AND DOCKING STUDIES OF NOVEL QUINAZOLINE ANALOGUES AS ORAL INHIBITORS TOWARDS AP-1 AND NF-κB

2009 ◽  
Vol 08 (03) ◽  
pp. 373-384
Author(s):  
LI QIAN ◽  
HAI-LIANG LU ◽  
SI-YAN LIAO ◽  
TI-FANG MIAO ◽  
YONG SHEN ◽  
...  
Keyword(s):  
Molecular Design ◽  
Three Dimensional ◽  
Predictive Ability ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Field Analysis ◽  
Activator Protein 1 ◽  
Mechanism Analysis ◽  
Qsar Study

Three-dimensional quantitative structure-activity relationship (3D-QSAR) and Docking studies of novel quinazoline analogues, which are oral potential inhibitors towards the activator protein-1 (AP-1) and nuclear factor kappa B (NF-κB), have been carried out. The 3D-QSAR study based on the comparative molecular field analysis (CoMFA) shows the established model having a significant statistical quality and excellent predictive ability, in which the correlation coefficient R is 0.972 and cross-validation coefficient q2 is 0.619. The Docking results also show a considerable correlation (or trend) between the energy scores and the corresponding experimental values for these compounds at some sites. Meanwhile, it is very interesting to find the binding sites just fall on the joint regions between AP-1 (or NF-κB) and DNA. It may be the reason that the quinazoline analogues have inhibition function because their existence on these joint regions can effectively prevent free AP-1 and NF-κB from binding to DNA. Based on the established 3D-QSAR and Docking analyses, six new compounds of quinazoline analogues with higher inhibitory activities were theoretically designed and presented. The above results can offer some valuable theoretical references for the pharmaceutical molecular design as well as the action mechanism analysis.

scholarly journals Integration of Fuzzy Matter-Element Method and 3D-QSAR Model for Generation of Environmentally Friendly Quinolone Derivatives

2020 ◽  
Vol 17 (9) ◽  
pp. 3239
Author(s):  
Xixi Li ◽  
Baiyu Zhang ◽  
Wendy Huang ◽  
Cuirin Cantwell ◽  
Bing Chen
Keyword(s):  
Density Functional ◽  
Molecular Design ◽  
Environmentally Friendly ◽  
3D Qsar ◽  
Qsar Model ◽  
Spectral Intensity ◽  
Field Analysis ◽  
Genetic Toxicity ◽  
Quinolone Derivatives ◽  
Element Method

The environmental pollution of quinolone antibiotics (QAs) has caused rising public concern due to their widespread usage. In this study, Gaussian 09 software was used to obtain the infrared spectral intensity (IRI) and ultraviolet spectral intensity (UVI) of 24 QAs based on the Density Functional Theory (DFT). Rather than using two single-factor inputs, a fuzzy matter-element method was selected to calculate the combined effects of infrared and ultraviolet spectra (CI). The Comparative Molecular Field Analysis (CoMFA) was then used to construct a three-dimensional quantitative structure–activity relationship (3D-QSAR) with QAs’ molecular structure as the independent variable and CI as the dependent variable. Using marbofloxacin and levofloxacin as target molecules, the molecular design of 87 QA derivatives was carried out. The developed models were further used to determine the stability, functionality (genetic toxicity), and the environmental effects (bioaccumulation, biodegradability) of these designed QA derivatives. Results indicated that all QA derivatives are stable in the environment with their IRI, UVI, and CI enhanced. Meanwhile, the genetic toxicity of the 87 QA derivatives increased by varying degrees (0.24%–29.01%), among which the bioaccumulation and biodegradability of 43 QA derivatives were within the acceptable range. Through integration of fuzzy matter-element method and 3D-QSAR, this study advanced the QAs research with the enhanced CI and helped to generate the proposed environmentally friendly quinolone derivatives so as to aid the management of this class of antibiotics.

2D/3D-QSAR STUDY ON ANALOGUES OF 2-METHOXYESTRADIOL WITH ANTICANCER ACTIVITY

2008 ◽  
Vol 07 (02) ◽  
pp. 287-301 ◽  
Author(s):  
SI YAN LIAO ◽  
LI QIAN ◽  
JIN CAN CHEN ◽  
YONG SHEN ◽  
KANG CHENG ZHENG
Keyword(s):  
Anticancer Activity ◽  
Molecular Design ◽  
Predictive Ability ◽  
Decisive Role ◽  
3D Qsar ◽  
Qsar Model ◽  
Field Analysis ◽  
Qsar Study ◽  
Comfa Model ◽  
2D Qsar

Two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationships (QSARs) of 23 analogs of 2-Methoxyestradiol with anticancer activity (expressed as p GI50) against MCF-7 human breast cancer cells have been studied by using a combined method of the DFT, MM2 and statistics for 2D, as well as the comparative molecular field analysis (CoMFA) for 3D. The established 2D-QSAR model in training set shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient [Formula: see text] and the square of the cross-validation coefficient (q2= 0.779). The same model was further applied to predict p GI50values of the four compounds in the test set, and the resulting [Formula: see text] being as high as 0.827, further confirms that this 2D-QSAR model has high predictive ability for this kind of compound. The 3D-QSAR model also shows good correlative and predictive capabilities in terms of R2(0.927) and q2(0.786) obtained from CoMFA model. The results that 2D- and 3D-QSAR analyses accord with each other, suggest that the electrostatic interaction plays a decisive role in determining the anticancer activity of the studied compounds, and that increasing the negative charge of substituent R2and the positive charge of substituents linking to C17as well as decreasing the size of substituent R1are advantageous to improving the cytotoxicity. Such results can offer some useful theoretical references for directing the molecular design and understanding the action mechanism of this kind of compound with anticancer activity.

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