A new, fully validated and interpreted quantitative structure-activity relationship model of p-aminosalicylic acid derivatives as neuraminidase inhibitors

2013 ◽  
Vol 67 (5) ◽  
Author(s):  
Ana Hartmman ◽  
Daniela Jornada ◽  
Eduardo Melo
Keyword(s):  
Predictive Ability ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Neuraminidase Inhibitors ◽  
Aminosalicylic Acid ◽  
Randomization Tests ◽  
Qsar Study ◽  
Relationship Model ◽  
Mechanism Of Interaction

AbstractA multivariate QSAR study with a set of 34 p-aminosalicylic acid derivatives, described as neuraminidase inhibitors of the H1N1 viruses, is presented in this work. The variable selection was performed with the Ordered Predictors Selection (OPS) algorithm and the model was built with the Partial Least Squares (PLS) regression method. Leave-N-out cross-validation and y-randomization tests showed that the model was robust and free from chance correlation. The external predictive ability was superior to the 3D-QSAR model previously published. Moreover, it was possible to perform a mechanistic interpretation, where the descriptors referred directly to the mechanism of interaction with the neuraminidase.

CoMFA, CoMSIA and HQSAR Analysis of 3-aryl-3-ethoxypropanoic Acid Derivatives as GPR40 Modulators

2020 ◽  
Vol 17 (1) ◽  
pp. 100-118
Author(s):  
Krishna A. Gajjar ◽  
Anuradha K. Gajjar
Keyword(s):  
Biological Activity ◽  
Structural Information ◽  
Three Dimensional ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Docking Studies ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Negative Contribution

Background: Human GPR40 receptor, also known as free fatty-acid receptor 1, is a Gprotein- coupled receptor that binds long chain free fatty acids to enhance glucose-dependent insulin secretion. In order to improve the resistance and efficacy, computational tools were applied to a series of 3-aryl-3-ethoxypropanoic acid derivatives. A relationship between the structure and biological activity of these compounds, was derived using a three-dimensional quantitative structure-activity relationship (3D-QSAR) study using CoMFA, CoMSIA and two-dimensional QSAR study using HQSAR methods. Methods: Building the 3D-QSAR models, CoMFA, CoMSIA and HQSAR were performed using Sybyl-X software. The ratio of training to test set was kept 70:30. For the generation of 3D-QSAR model three different alignments were used namely, distill, pharmacophore and docking based alignments. Molecular docking studies were carried out on designed molecules using the same software. Results: Among all the three methods used, Distill alignment was found to be reliable and predictive with good statistical results. The results obtained from CoMFA analysis q2, r2cv and r2 pred were 0.693, 0.69 and 0.992 respectively and in CoMSIA analysis q2, r2cv and r2pred were 0.668, 0.648 and 0.990. Contour maps of CoMFA (lipophilic and electrostatic), CoMSIA (lipophilic, electrostatic, hydrophobic, and donor) and HQSAR (positive & negative contribution) provided significant insights i.e. favoured and disfavoured regions or positive & negative contributing fragments with R1 and R2 substitutions, which gave hints for the modifications required to design new molecules with improved biological activity. Conclusion: 3D-QSAR techniques were applied for the first time on the series 3-aryl-3- ethoxypropanoic acids. All the models (CoMFA, CoMSIA and HQSAR) were found to be satisfactory according to the statistical parameters. Therefore such a methodology, whereby maximum structural information (from ligand and biological target) is explored, gives maximum insights into the plausible protein-ligand interactions and is more likely to provide potential lead candidates has been exemplified from this study.

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 In silico Prediction of Novel SARS-CoV 3CL pro Inhibitors: a Combination of 3D-QSAR, Molecular Docking, ADMET Prediction, and Molecular Dynamics Simulation

2021 ◽  
Vol 12 (4) ◽  
pp. 5100-5115
Keyword(s):  
Molecular Dynamics ◽  
Molecular Docking ◽  
Predictive Ability ◽  
3D Qsar ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Dynamics Simulation ◽  
Qsar Study ◽  
Determination Coefficient ◽  
Pharmacokinetic Properties

The Chymotrypsin-like protease (3CLpro) is a drug target in the coronavirus because of its role in processing the polyproteins that are translated from the viral RNA. This study applied 3D quantitative structure-activity relationship (3D-QSAR), molecular docking, and ADMET prediction on a series of SARS-CoV 3CLpro inhibitors. The 3D-QSAR study was applied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) methods, which gave the cross-validation coefficient (Q2) values of 0.64 and 0.80, the determination coefficient (R2) values of 0.998 and 0.993 and the standard error of the estimate (SEE) values of 0.046 and 0.091, respectively. The acceptable values of the determination coefficient (R2 test) to CoMFA and CoMSIA respectively corresponding to values of 0.725 and 0.690 utilizing a test set of seven molecules prove the high predictive ability of this model. Molecular docking analysis was utilized to validate 3D-QSAR methods and explain the binding site interactions and affinity between the most active ligands and the SARS-CoV 3CLpro receptor. Based on these results, a novel series of compounds were predicted, and their pharmacokinetic properties were verified using drug-likeness and ADMET prediction. Finally, the best-docked candidate molecules were subjected to molecular dynamics (MD) simulation to affirm their dynamic behavior and stability.

Design, synthesis, antifungal activity and 3D-QSAR study of novel pyrazole carboxamide and niacinamide derivatives containing benzimidazole moiety

2019 ◽  
Vol 43 (7) ◽  
pp. 3000-3010 ◽  
Author(s):  
Wei-Jie Si ◽  
Xiao-Bin Wang ◽  
Min Chen ◽  
Meng-Qi Wang ◽  
Ai-Min Lu ◽  
...  
Keyword(s):  
Antifungal Activity ◽  
Predictive Ability ◽  
3D Qsar ◽  
Qsar Model ◽  
Qsar Study ◽  
Design Synthesis ◽  
Effective Inhibition

The synthesized pyrazole carboxamide and niacinamide derivatives containing a benzimidazole moiety showed effective inhibition of the fungus B. cinereal growth. The 3D-QSAR model was built and revealed fine predictive ability.

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 Synthesis of Bioactive Compounds from 3-Carene (II): Synthesis, Antifungal Activity and 3D-QSAR Study of (Z)- and (E)-3-Caren-5-One Oxime Sulfonates

Molecules ◽  
2019 ◽  
Vol 24 (3) ◽  
pp. 477 ◽  
Author(s):  
Guo-Qiang Kang ◽  
Wen-Gui Duan ◽  
Gui-Shan Lin ◽  
You-Pei Yu ◽  
Xiao-Yu Wang ◽  
...  
Keyword(s):  
Antifungal Activity ◽  
Three Dimensional ◽  
Alternaria Solani ◽  
3D Qsar ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Cercospora Arachidicola ◽  
Qsar Study ◽  
In Vitro Antifungal Activity

A series of novel (Z)- and (E)-3-caren-5-one oxime sulfonates were designed and synthesized in search of potent antifungal agents. The structures of the intermediates and target compounds were confirmed by UV-Vis, FTIR, NMR, and ESI-MS. The in vitro antifungal activity of the target compounds was preliminarily evaluated against Cercospora arachidicola, Physalospora piricola, Alternaria solani, Rhizoeotnia solani, Bipolaris maydis and Colleterichum orbicalare at 50 µg/mL. The bioassay results indicated that the target compounds exhibited the best antifungal activity against P. piricola, in which compounds 4b, 4f, 4m, 4e, 4j, 4l, 4y, 4d, and 4p had excellent inhibition rates of 100%, 100%, 100%, 92.9%, 92.9%, 92.9%, 92.9%, 85.7%, and 85.7%, respectively, showing much better antifungal activity than that of the commercial fungicide chlorothanil. Both the compounds 4y and 4x displayed outstanding antifungal activity of 100% against B. myadis, and the former also displayed outstanding antifungal activity of 100% against R. solani. In order to design more effective antifungal compounds against P. piricola, the analysis of three-dimensional quantitative structure-activity relationship (3D-QSAR) was carried out using the CoMFA method, and a reasonable and effective 3D-QSAR model (r2 = 0.990, q2 = 0.569) has been established.

3D-QSAR Studies of S-DABO Derivatives as Non-nucleoside HIV-1 Reverse Transcriptase Inhibitors

2019 ◽  
Vol 16 (8) ◽  
pp. 868-881
Author(s):  
Yueping Wang ◽  
Jie Chang ◽  
Jiangyuan Wang ◽  
Peng Zhong ◽  
Yufang Zhang ◽  
...  
Keyword(s):  
Reverse Transcriptase ◽  
Three Dimensional ◽  
Predictive Ability ◽  
3D Qsar ◽  
Binding Mode ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Reverse Transcriptase Inhibitors ◽  
Qsar Studies ◽  
Hiv 1

Background: S-dihydro-alkyloxy-benzyl-oxopyrimidines (S-DABOs) as non-nucleoside reverse transcriptase inhibitors have received considerable attention during the last decade due to their high potency against HIV-1. Methods: In this study, three-dimensional quantitative structure-activity relationship (3D-QSAR) of a series of 38 S-DABO analogues developed in our lab was studied using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). The Docking/MMFF94s computational protocol based on the co-crystallized complex (PDB ID: 1RT2) was used to determine the most probable binding mode and to obtain reliable conformations for molecular alignment. Statistically significant CoMFA (q2=0.766 and r2=0.949) and CoMSIA (q2=0.827 and r2=0.974) models were generated using the training set of 30 compounds on the basis of hybrid docking-based and ligand-based alignment. Results: The predictive ability of CoMFA and CoMSIA models was further validated using a test set of eight compounds with predictive r2 pred values of 0.843 and 0.723, respectively. Conclusion: The information obtained from the 3D contour maps can be used in designing new SDABO derivatives with improved HIV-1 inhibitory activity.

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.

THE EXPLORATION OF CYP17A1 LIGAND SPACE BY THE QSAR MODEL

2021 ◽  
Author(s):  
Natalia Boboriko ◽  
◽  
He Liying ◽  
Yaraslau Dzichenka
Keyword(s):  
Hydrophobic Effect ◽  
Predictive Ability ◽  
Distance Matrix ◽  
Qsar Model ◽  
Aromatic Rings ◽  
Qsar Study ◽  
Test Set ◽  
Highly Active ◽  
High Efficient ◽  
F Measure

Cytochrome P450 17A1 (CYP17A1) is a critically important enzyme in humans that catalyzes the formation of all endogenous androgens. This enzyme is often considered a molecular target for the development of novel high efficient drugs against prostate cancer. In the present work, the random forest algorithm was used to conduct a QSAR study on 370 CYP17A1 ligands with different structures that were collected from the literature and databases, and a QSAR model was created based on the five important descriptors screened out – 2D adjacency and distance matrix descriptors, 2D atom counts and bond counts and 3D surface area, volume and shape descriptors. The model was verified by the test set (accuracy, specificity, sensitivity, F-measure, MCC, and AUC were calculated). It was revealed that the hydrophobic properties of the vdW surface of the ligand have a significant contribution to the activity prediction. The hydrophobic effect of the molecules may be aroused by the presence of the hydrophobic groups or aromatic rings in the molecules. The created QSAR model shows that the molecules with more aromatic rings have better activity. The accuracy of the model on the test set was 84%, precision – 81%, sensitivity – 93%, specificity – 72%, F-measure – 0.87, MCC – 0.67, AUC – 0.88. The model has good robustness and predictive ability and can be used to screen and discover new highly active CYP17A1 inhibitors.

scholarly journals A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Li Wen ◽  
Qing Li ◽  
Wei Li ◽  
Qiao Cai ◽  
Yong-Ming Cai
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
Nonlinear Models ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Qsar Study ◽  
Stability Robustness ◽  
Qsar Models ◽  
Leave One Out

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEPext), fitting correlation coefficient (R2), and leave-one-out cross-validation (Q2LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R2 and Q2LOO of 4 kinds of nonlinear models are more than 0.6 and SDEPext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R2=0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.

Sign in / Sign up

Export Citation Format

Share Document