scholarly journals 3D-QSAR Study of Indol-2-yl Ethanones Derivatives as Novel Indoleamine 2,3-Dioxygenase (IDO) Inhibitors

2012 ◽  
Vol 9 (4) ◽  
pp. 1753-1759 ◽  
Author(s):  
Kamlendra S. Bhadoriya ◽  
Shailesh V. Jain ◽  
Sanjaykumar B. Bari ◽  
Manish L. Chavhan ◽  
Kuldeep R. Vispute
Keyword(s):  
Nearest Neighbor ◽  
External Validation ◽  
3D Qsar ◽  
Qsar Model ◽  
Field Analysis ◽  
Qsar Study ◽  
Internal Validation ◽  
Contour Maps ◽  
Model Classification ◽  
Ido Inhibitors

3D-QSAR approach usingkNN-MFA was applied to a series of Indol-2-yl ethanones derivatives as novel IDO inhibitors. For the purpose, 22 compounds were used to develop models. To elucidate the structural properties required for IDO inhibitory activity, we report herek-nearest neighbor molecular field analysis (kNN-MFA)-based 3D-QSAR model for Indol-2-yl ethanones derivatives as novel IDO inhibitors. Overall model classification accuracy was 76.27% (q2= 0.7627, representing internal validation) in training set and 79.35% (pred_r2= 0.7935, representing external validation) in test set using sphere exclusion and forward as a method of data selection and variable selection, respectively. Contour maps using this approach showed that hydrophobic and steric effects dominantly determine binding affinities. The information rendered by 3D-QSAR model may lead to a better understanding of structural requirements of IDO inhibitors and can help in the design of novel potent molecules.

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.

PREDICTIVE QSAR MODELING OF PYRIDAZINYL DERIVATIVES USING K-NEAREST NEIGHBOR AND PHARMACOPHORE APPROACH

INDIAN DRUGS ◽  
2017 ◽  
Vol 54 (07) ◽  
pp. 10-17
Author(s):  
M.C. Sharma ◽  
◽  
D.V. Kohli
Keyword(s):  
Correlation Coefficient ◽  
Nearest Neighbor ◽  
Predictive Ability ◽  
3D Qsar ◽  
Qsar Model ◽  
Field Analysis ◽  
Angiotensin Ii Receptor ◽  
K Nearest Neighbor ◽  
Qsar Modeling ◽  
Leave One Out

This study was carried out elucidate the structural properties required for pyridazinyl derivatives to exhibit angiotensin II receptor activity. The best 2D-QSAR model was selected, having correlation coefficient r2 = 0.8156, cross validated squared correlation coefficient q2 = 0.7348 and predictive ability of the selected model was also confirmed by leave one out cross validation method. Further analysis was carried out using 3D-QSAR method k-nearest neighbor molecular field analysis approach; a leave-one-out crossvalidated correlation coefficient of 0.7188 and a predictivity for the external test set (0.7613) were obtained. By studying the QSAR models, one can select the suitable substituent for active compound with maximum potency.

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.

DEVELOPMENT OF A CREDIBLE QSAR AND K NEAREST NEIGHBOR MODELS FOR IMIDAZOLYL DERIVATIVES

INDIAN DRUGS ◽  
2018 ◽  
Vol 55 (05) ◽  
pp. 7-13
Author(s):  
M. C Sharma ◽  
Keyword(s):  
Correlation Coefficient ◽  
Nearest Neighbor ◽  
Antihypertensive Agents ◽  
3D Qsar ◽  
Qsar Model ◽  
Structural Features ◽  
Field Analysis ◽  
Molecular Field ◽  
K Nearest Neighbor ◽  
Molecular Field Analysis

Quantitative Structure-Activity Relationship studies were performed for correlating the imidazolyl derivatives and their activity using molecular modeling studies. The statistically significant best 2D model was having correlation coefficient = 0.8221 and cross-validated squared correlation coefficient = 0.7534 with external predictive ability of pred_r2 = 0.7716. Molecular field analysis was used to construct the best 3D-QSAR model showing good correlative and predictive capabilities in terms of q2 =0.6781 and pred_r2 =0.7299. The molecular field analysis (MFA) contour plots provided further understanding of the relationship between structural features of Imidazolyl derivatives and their activities which should be applicable to design newer potential antihypertensive agents.

scholarly journals Investigating the Molecular Basis of N-Substituted 1-Hydroxy-4-Sulfamoyl-2-Naphthoate Compounds Binding to Mcl1

Processes ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 224 ◽  
Author(s):  
Singaravelu ◽  
Balasubramanian ◽  
Marimuthu
Keyword(s):  
Molecular Mechanism ◽  
Md Simulation ◽  
External Validation ◽  
Myeloid Cell ◽  
3D Qsar ◽  
Qsar Model ◽  
Maximum Activity ◽  
Hydrogen Bond Donor ◽  
Contour Maps ◽  
Substituted 1

Myeloid cell leukemia-1 (Mcl1) is an anti–apoptotic protein that has gained considerable attention due to its overexpression activity prevents cell death. Therefore, a potential inhibitor that specifically targets Mcl1 with higher binding affinity is necessary. Recently, a series of N-substituted 1-hydroxy-4-sulfamoyl-2-naphthoate compounds was reported that targets Mcl1, but its binding mechanism remains unexplored. Here, we attempted to explore the molecular mechanism of binding to Mcl1 using advanced computational approaches: pharmacophore-based 3D-QSAR, docking, and MD simulation. The selected pharmacophore—NNRRR—yielded a statistically significant 3D-QSAR model containing high confidence scores (R2 = 0.9209, Q2 = 0.8459, and RMSE = 0.3473). The contour maps—comprising hydrogen bond donor, hydrophobic, negative ionic and electron withdrawal effects—from our 3D-QSAR model identified the favorable regions crucial for maximum activity. Furthermore, the external validation of the selected model using enrichment and decoys analysis reveals a high predictive power. Also, the screening capacity of the selected model had scores of 0.94, 0.90, and 8.26 from ROC, AUC, and RIE analysis, respectively. The molecular docking of the highly active compound—C40; 4-(N-benzyl-N-(4-(4-chloro-3,5-dimethylphenoxy) phenyl) sulfamoyl)-1-hydroxy-2-naphthoate—predicted the low-energy conformational pose, and the MD simulation revealed crucial details responsible for the molecular mechanism of binding with Mcl1.

QSAR Analysis for Antioxidant Activity of Dipicolinic Acid Derivatives

2018 ◽  
Vol 21 (3) ◽  
pp. 204-214 ◽  
Author(s):  
Vesna Rastija ◽  
Maja Molnar ◽  
Tena Siladi ◽  
Vijay Hariram Masand
Keyword(s):  
Antioxidant Activity ◽  
Antioxidant Activities ◽  
Dipicolinic Acid ◽  
External Validation ◽  
Qsar Model ◽  
Internal Validation ◽  
Qsar Analysis ◽  
Dragon Descriptors ◽  
Qsar Models ◽  
New Compounds

Aims and Objectives: The aim of this study was to derive robust and reliable QSAR models for clarification and prediction of antioxidant activity of 43 heterocyclic and Schiff bases dipicolinic acid derivatives. According to the best obtained QSAR model, structures of new compounds with possible great activities should be proposed. Methods: Molecular descriptors were calculated by DRAGON and ADMEWORKS from optimized molecular structure and two algorithms were used for creating the training and test sets in both set of descriptors. Regression analysis and validation of models were performed using QSARINS. Results: The model with best internal validation result was obtained by DRAGON descriptors (MATS4m, EEig03d, BELm4, Mor10p), split by ranking method (R2 = 0.805; R2 ext = 0.833; F = 30.914). The model with best external validation result was obtained by ADMEWORKS descriptors (NDB, MATS5p, MDEN33, TPSA), split by random method (R2 = 0.692; R2 ext = 0.848; F = 16.818). Conclusion: Important structural requirements for great antioxidant activity are: low number of double bonds in molecules; absence of tertial nitrogen atoms; higher number of hydrogen bond donors; enhanced molecular polarity; and symmetrical moiety. Two new compounds with potentially great antioxidant activities were proposed.

Computational Atom-based Three-Dimensional Quantitative Structure-Activity Relationship (3D QSAR) Model for Predicting Anti-Dengue Agents

2021 ◽  
Vol 16 (10) ◽  
pp. 50-58
Author(s):  
Ali Qusay Khalid ◽  
Vasudeva Rao Avupati ◽  
Husniza Hussain ◽  
Tabarek Najeeb Zaidan
Keyword(s):  
Dengue Fever ◽  
Three Dimensional ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Contour Maps ◽  
Structure Activity ◽  
Bioactive Ligands

Dengue fever is a viral infection spread by the female mosquito Aedes aegypti. It is a virus spread by mosquitoes found all over the tropics with risk levels varying depending on rainfall, relative humidity, temperature and urbanization. There are no specific medications that can be used to treat the condition. The development of possible bioactive ligands to combat Dengue fever before it becomes a pandemic is a global priority. Few studies on building three-dimensional quantitative structure-activity relationship (3D QSAR) models for anti-dengue agents have been reported. Thus, we aimed at building a statistically validated atom-based 3D-QSAR model using bioactive ligands reported to possess significant anti-dengue properties. In this study, the Schrodinger PhaseTM atom-based 3D QSAR model was developed and was validated using known anti-dengue properties as ligand data. This model was also tested to see if there was a link between structural characteristics and anti-dengue activity of a series of 3-acyl-indole derivatives. The established 3D QSAR model has strong predictive capacity and is statistically significant [Model: R2 Training Set = 0.93, Q2 (R2 Test Set) = 0.72]. In addition, the pharmacophore characteristics essential for the reported anti-dengue properties were explored using combined effects contour maps (coloured contour maps: blue: positive potential and red: negative potential) of the model. In the pathway of anti-dengue drug development, the model could be included as a virtual screening method to predict novel hits.

scholarly journals 3D QSAR AND DOCKING STUDY OF INDOLE DERIVATIVES AS SELECTIVE COX-2 INHIBITORS

2019 ◽  
pp. 84-92
Author(s):  
Rajashree Chavan ◽  
HARINATH MORE
Keyword(s):  
Binding Affinity ◽  
Correlation Coefficient ◽  
3D Qsar ◽  
Docking Study ◽  
Field Analysis ◽  
Indole Derivatives ◽  
K Nearest Neighbor ◽  
Qsar Study ◽  
Cox 2 ◽  
Anti Inflammatory

Objective: Non-steroidal anti-inflammatory agents (NSAIDs) continue to be one of the most widely used groups of therapeutic agents. QSAR (quantitative structure-activity relationship) approach is a very useful and widespread technique for drug design. 3D QSAR facilitates evaluation of three-dimensional molecular fields around molecules and generates a relationship of these fields' values with the activity. Methods: 3D QSAR study was performed on selected twenty-four compounds from synthesized indole derivatives using the stepwise variable selection k-nearest neighbor (kNN) molecular field analysis approach for indicating the contribution of the steric and electronic field for activity. The docking study was performed to further confirm the binding affinity of synthesized molecules (ligands) to COX-2 enzyme as well as to study binding nature. Results: Statistically significant model was generated using VLife Molecular Design Suite 3.5 software with cross-validated correlation coefficient q2 of 0.9461 and high predictive correlation coefficient (Pred_r2) of 0.8782 indicating that the model is robust. The results of docking study suggest that the synthesized compounds have a comparable binding affinity with the COX-2 enzyme. Conclusion: The present study may prove to be helpful in the development and optimization of existing indole derivatives as anti-inflammatory agents with selective COX-2 inhibition.

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

2014 ◽  
Vol 39 (2) ◽  
pp. 191-199 ◽  
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

Sign in / Sign up

Export Citation Format

Share Document