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.

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).

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.

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.

scholarly journals Conformational analysis and molecular design of anthranilic acid derivatives as partial agonists of the Farnesoid X Receptor (FXR) with favorable predicted pharmacokinetic profiles

2021 ◽  
Vol 5 (2) ◽  
pp. 585-600
Author(s):  
Guy Müller Banquet OKRA ◽  
◽  
Dali Brice ◽  
Hermann N'Guessan ◽  
Affiba Florance Kouassi ◽  
...  
Keyword(s):  
Anthranilic Acid ◽  
Molecular Design ◽  
Pharmacophore Model ◽  
3D Qsar ◽  
Qsar Model ◽  
Farnesoid X Receptor ◽  
Virtual Design ◽  
Ligand Complex ◽  
Pharmacokinetic Profiles ◽  
Anthranilic Acid Derivatives

We report here virtual design of new anthranilic acid derivatives (AAD) identified as potent partial Farnesoid X recep-tor (FXR) agonists with favorable predicted pharmacokinetic profiles. By in situ modification of the crystal structure (PDB ID: 3OLF) of FXR complex with a benzimidazole-based partial agonistic ligand, 3D models of 17 FXR:AADx complexes with known observed activity (EC50exp) were prepared to establish a quantitative structure–activity (QSAR) model and linear correla-tion between relative Gibbs free energy (GFE) of receptor-ligand complex formation (Gcom) and EC50exp: pEC50exp = -0,1146 Gcom + 8,175 (#); R2 = 0.98. A 3D QSAR pharmacophore model (PH4) derived from the QSAR directed our effort to design novel AAD analogs. During the design, an initial virtual library of 94501 AAD was focused down to 33134 drug-like compounds and finally, PH4 screened to identify 100 promising compounds. Theoretical EC50 (EC50pre) values of all analogs compounds were predicted by means of equation (#) and their pharmacokinetics (ADME) profiles were computed. More than 12 putative AADs display EC50pre 300 times superior to that of the reported most active training set inhibitor AAD1.

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.

scholarly journals Environmentally Friendly Quinolones Design for a Two-Way Choice between Biotoxicity and Genotoxicity through Double-Activity 3D-QSAR Model Coupled with the Variation Weighting Method

2020 ◽  
Vol 17 (24) ◽  
pp. 9398
Author(s):  
Peixuan Sun ◽  
Yuanyuan Zhao ◽  
Luze Yang ◽  
Zhixing Ren ◽  
Wenjin Zhao
Keyword(s):  
Molecular Dynamics ◽  
Structural Parameters ◽  
Environmentally Friendly ◽  
3D Qsar ◽  
Qsar Model ◽  
Binding Energies ◽  
Combined Toxicity ◽  
Weighting Method ◽  
Environmental Friendliness ◽  
Before And After

Quinolone (QN) antibiotics are widely used, which lead to their accumulation in soil and toxic effects on ryegrass in pasture. In this study, we employed ryegrass as the research object and selected the total scores of 29 QN molecules docked with two resistant enzyme structures, superoxide dismutase (SOD, PDB ID: 1B06) and proline (Pro, PPEP-2, PDB ID: 6FPC), as dependent variables. The structural parameters of QNs were used as independent variables to construct a QN double-activity 3D-QSAR model for determining the biotoxicity on ryegrass by employing the variation weighting method. This model was constructed to determine modification sites and groups for designing QNs molecules. According to the 3D contour map of the model, by considering enrofloxacin (ENR) and sparfloxacin (SPA) as examples, 23 QN derivatives with low biotoxicity were designed, respectively. The functional properties and environmental friendliness of the QN derivatives were predicted through a two-way selection between biotoxicity and genotoxicity before and after modification; four environmentally friendly derivatives with low biotoxicity and high genotoxicity were screened out. Mixed toxicity index and molecular dynamics methods were used to verify the combined toxicity mechanism of QNs on ryegrass before and after modification. By simulating the combined pollution of ENR and its derivatives in different soils (farmland, garden, and woodland), the types of combined toxicity were determined as partial additive and synergistic. Binding energies were calculated using molecular dynamics. The designed QN derivatives with low biotoxicity, high genotoxicity, and environmental friendliness can highly reduce the combined toxicity on ryegrass and can be used as theoretic reserves to replace QN antibiotics.

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.

scholarly journals Potential Environmental Risk Characteristics of PCB Transformation Products in the Environmental Medium

Toxics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 213
Author(s):  
Minghao Li ◽  
Wei He ◽  
Hao Yang ◽  
Shimei Sun ◽  
Yu Li
Keyword(s):  
Environmental Risk ◽  
Polychlorinated Biphenyl ◽  
Degradation Products ◽  
Organic Pollutant ◽  
Environmentally Friendly ◽  
3D Qsar ◽  
Transformation Products ◽  
Qsar Model ◽  
Environmental Risks ◽  
Environmental Media

The complementary construction of polychlorinated biphenyl (PCB) phytotoxicity and the biotoxicity 3D-QSAR model, combined with the constructed PCB environmental risk characterization model, was carried out to evaluate the persistent organic pollutant (POP) properties (toxicity (phytotoxicity and biotoxicity), bioconcentration, migration, and persistence) of PCBs and their corresponding transformation products (phytodegradation, microbial degradation, biometabolism, and photodegradation). The transformation path with a significant increase in environmental risks was analyzed. Some environmentally friendly PCB derivatives, exhibiting a good modification effect, and their parent molecules were selected as precursor molecules. Their transformation processes were simulated and evaluated for assessing the environmental risks. Some transformation products displayed increased environmental risks. The environmental risks of plant degradation products of the PCBs in the environmental media showed the maximum risk, indicating that the potential risks of the transformation products of the PCBs and their environmentally friendly derivatives could not be neglected. It is essential to further improve the ability of plants to degrade their transformation products. The improvement of some degradation products for environmentally friendly PCB derivatives indicates that the theoretical modification of a single environmental feature cannot completely control the potential environmental risks of molecules. In addition, this method can be used to analyze and evaluate environmentally friendly PCB derivatives to avoid and reduce the potential environmental and human health risks caused by environmentally friendly PCB derivatives.

Sign in / Sign up

Export Citation Format

Share Document