scholarly journals Design of New Potent Insecticides of Organophosphate Derivatives Based on QSAR Analysis

2013 ◽  
Vol 13 (1) ◽  
pp. 86-93 ◽  
Author(s):  
Mudasir Mudasir ◽  
Yari Mukti Wibowo ◽  
Harno Dwi Pranowo
Keyword(s):  
Sulfonic Acid ◽  
Molecular Descriptors ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Benzene Sulfonic Acid ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Qsar Models ◽  
New Compounds

Design of new potent insecticide compounds of organophosphate derivatives based on QSAR (Quantitative Structure-Activity Relationship) analytical model has been conducted. Organophosphate derivative compounds and their activities were obtained from the literature. Computational modeling of the structure of organophosphate derivative compounds and calculation of their QSAR descriptors have been done by AM1 (Austin Model 1) method. The best QSAR model was selected from the QSAR models that used only electronic descriptors and from those using both electronic and molecular descriptors. The best QSAR model obtained was:Log LD50 = 50.872 - 66.457 qC1 - 65.735 qC6 + 83.115 qO7 (n = 30, r = 0.876, adjusted r2 = 0.741, Fcal/Ftab = 9.636, PRESS = 2.414 x 10-6)The best QSAR model was then used to design in silico new compounds of insecticide of organophosphate derivatives with better activity as compared to the existing synthesized organophosphate derivatives. So far, the most potent insecticide of organophosphate compound that has been successfully synthesized had log LD50 of -5.20, while the new designed compound based on the best QSAR model, i.e.: 4-(diethoxy phosphoryloxy) benzene sulfonic acid, had log LD50 prediction of -7.29. Therefore, the new designed insecticide compound is suggested to be synthesized and tested for its activity in laboratory for further verification.

scholarly journals Quantitative Structure-Activity Relationship Studies for Potential Rho-Associated Protein Kinase Inhibitors

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Giovanna Cardoso Gajo ◽  
Tamiris Maria de Assis ◽  
Letícia Cristina Assis ◽  
Teodorico Castro Ramalho ◽  
Elaine Fontes Ferreira da Cunha
Keyword(s):  
Protein Kinase ◽  
Kinase Inhibitors ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Protein Kinase Inhibitors ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Qsar Analysis ◽  
Structure Activity ◽  
New Compounds

A series of pyridylthiazole derivatives developed by Lawrence et al. as Rho-associated protein kinase inhibitors were subjected to four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis. The models were generated applying genetic algorithm (GA) optimization combined with partial least squares (PLS) regression. The best model presented validation values ofr2=0.773,qCV2=0.672,rpred2=0.503,Δrm2=0.197,rm test2⁡⁡=0.520,rY-rand2=0.19, andRp2=0.590. Furthermore, analyzing the descriptors it was possible to propose new compounds that predicted higher inhibitory concentration values than the most active compound of the series.

scholarly journals Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

2020 ◽  
Author(s):  
Vijay Masand ◽  
Ajaykumar Gandhi ◽  
Vesna Rastija ◽  
Meghshyam K. Patil
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Atom Pairs ◽  
Main Protease ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div><br></div>

scholarly journals Synthesis, Cytotoxic Activity Evaluation and Quantitative Structure-ActivityAnalysis of Substituted 5,8-Dihydroxy-1,4-naphthoquinones and Their O- and S-Glycoside Derivatives Tested against Neuro-2a Cancer Cells

Marine Drugs ◽  
2020 ◽  
Vol 18 (12) ◽  
pp. 602
Author(s):  
Sergey Polonik ◽  
Galina Likhatskaya ◽  
Yuri Sabutski ◽  
Dmitry Pelageev ◽  
Vladimir Denisenko ◽  
...  
Keyword(s):  
Cytotoxic Activity ◽  
Sea Urchins ◽  
Neuroblastoma Cells ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Mtt Method ◽  
Structure Activity ◽  
Cytotoxic Compounds ◽  
Qsar Models

Based on 6,7-substituted 2,5,8-trihydroxy-1,4-naphtoquinones (1,4-NQs) derived from sea urchins, five new acetyl-O-glucosides of NQs were prepared. A new method of conjugation of per-O-acetylated 1-mercaptosaccharides with 2-hydroxy-1,4-NQs through a methylene spacer was developed. Methylation of 2-hydroxy group of quinone core of acetylthiomethylglycosides by diazomethane and deacetylation of sugar moiety led to 28 new thiomethylglycosidesof 2-hydroxy- and 2-methoxy-1,4-NQs. The cytotoxic activity of starting 1,4-NQs (13 compounds) and their O- and S-glycoside derivatives (37 compounds) was determined by the MTT method against Neuro-2a mouse neuroblastoma cells. Cytotoxic compounds with EC50 = 2.7–87.0 μM and nontoxic compounds with EC50 > 100 μM were found. Acetylated O- and S-glycosides 1,4-NQs were the most potent, with EC50 = 2.7–16.4 μM. Methylation of the 2-OH group innaphthoquinone core led to a sharp increase in the cytotoxic activity of acetylated thioglycosidesof NQs, which was partially retained for their deacetylated derivatives. Thiomethylglycosides of 2-hydroxy-1,4-NQs with OH and MeO groups in quinone core at positions 6 and 7, resprectively formed a nontoxic set of compounds with EC50 > 100 μM. A quantitative structure-activity relationship (QSAR) model of cytotoxic activity of 22 1,4-NQ derivatives was constructed and tested. Descriptors related to the cytotoxic activity of new 1,4-NQ derivatives were determined. The QSAR model is good at predicting the activity of 1,4-NQ derivatives which are unused for QSAR models and nontoxic derivatives.

scholarly journals Comparison of Quantitative Structure-Activity Relationship Model Performances on Carboquinone Derivatives

2009 ◽  
Vol 9 ◽  
pp. 1148-1166 ◽  
Author(s):  
Sorana D. Bolboaca ◽  
Lorentz Jäntschi
Keyword(s):  
Goodness Of Fit ◽  
Molecular Descriptors ◽  
Information Criterion ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Information Criteria ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Qsar Models

Quantitative structure-activity relationship (qSAR) models are used to understand how the structure and activity of chemical compounds relate. In the present study, 37 carboquinone derivatives were evaluated and two different qSAR models were developed using members of the Molecular Descriptors Family (MDF) and the Molecular Descriptors Family on Vertices (MDFV). The usual parameters of regression models and the following estimators were defined and calculated in order to analyze the validity and to compare the models: Akaike?s information criteria (three parameters), Schwarz (or Bayesian) information criterion, Amemiya prediction criterion, Hannan-Quinn criterion, Kubinyi function, Steiger's Z test, and Akaike's weights. The MDF and MDFV models proved to have the same estimation ability of the goodness-of-fit according to Steiger's Z test. The MDFV model proved to be the best model for the considered carboquinone derivatives according to the defined information and prediction criteria, Kubinyi function, and Akaike's weights.

Descriptor Selection Improvements for Quantitative Structure-Activity Relationships

2019 ◽  
Vol 29 (09) ◽  
pp. 1950016 ◽  
Author(s):  
Liang-Yong Xia ◽  
Qing-Yong Wang ◽  
Zehong Cao ◽  
Yong Liang
Keyword(s):  
Molecular Descriptor ◽  
Area Under The Curve ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Small Subset ◽  
Quantitative Structure ◽  
Complex Samples ◽  
Structure Activity ◽  
Descriptor Selection ◽  
Qsar Models

Molecular descriptor selection is an essential procedure to improve a predictive quantitative structure–activity relationship (QSAR) model. However, within the QSAR model, there are a number of redundant, noisy and irrelevant descriptors. In this study, we propose a novel descriptor selection framework using self-paced learning (SPL) via sparse logistic regression (LR) with Logsum penalty (SPL-Logsum), which can simultaneously adaptively identify the simple and complex samples and avoid over-fitting. SPL is inspired by the learning process of humans or animals gradually learned from simple and complex samples to train models, and the Logsum penalized LR helps to select a small subset of significant molecular descriptors for improving the QSAR models. Experimental results on some simulations and three public QSAR datasets show that our proposed SPL-Logsum framework outperforms other existing sparse methods regarding the area under the curve, sensitivity, specificity, accuracy, and [Formula: see text]-values.

Multilayer Perceptron Model for Predicting Acute Toxicity of Fungicides on Rats

2018 ◽  
Vol 3 (1) ◽  
pp. 100-118 ◽  
Author(s):  
Mabrouk Hamadache ◽  
Abdeltif Amrane ◽  
Salah Hanini ◽  
Othmane Benkortbi
Keyword(s):  
Molecular Descriptors ◽  
External Validation ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Acute Oral Toxicity ◽  
Oral Toxicity ◽  
Structure Activity ◽  
Marquardt Algorithm ◽  
Qsar Models ◽  
Validation Set

Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, a QSAR model based on 10 molecular descriptors to predict acute oral toxicity of 91 fungicides to rats was developed and validated. Good results (PRESS/SSY = 0.085 and VIF < 5) were obtained, showing the validation of descriptors in the obtained model. The best results were obtained with a 10/11/1 Artificial Neural Network model trained with the Levenberg-Marquardt algorithm. The prediction accuracy for the external validation set was estimated by the Q2ext which was equal to 0.960. Accordingly, the model developed in this study provided excellent predictions and can be used to predict the acute oral toxicity of fungicides, particularly for those that have not been tested as well as new fungicides.

scholarly journals Structure Features of Peptide-Type SARS-CoV Main Protease Inhibitors: Quantitative Structure Activity Relationship Study

2020 ◽  
Author(s):  
Vijay Masand ◽  
Ajaykumar Gandhi ◽  
Vesna Rastija ◽  
Meghshyam K. Patil
Keyword(s):  
Quantitative Structure Activity Relationship ◽  
Structural Features ◽  
Quantitative Structure ◽  
Statistical Parameters ◽  
Qsar Analysis ◽  
Structure Activity ◽  
Atom Pairs ◽  
Main Protease ◽  
Qsar Models ◽  
Quantitative Structure Activity Relationships

<div>In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R2 = 0.80–0.82, Q2loo = 0.74–0.77). The developed models identified interrelations of atom pairs as important molecular descriptors. Therefore, the present QSAR models have a good balance of Qualitative and Quantitative approaches, thereby, useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.</div><div><br></div>

scholarly journals Prediction of Terpenoids Toxicity Based on Quantitative Structure-Activity Relationships Model

2019 ◽  
Author(s):  
Rosa Perestrelo ◽  
Catarina Silva ◽  
Miguel X. Fernandes ◽  
José S. Câmara
Keyword(s):  
Chemical Structure ◽  
Vibrio Fischeri ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Geranic Acid ◽  
Structure Activity ◽  
Coefficient Corrélation ◽  
Qsar Models ◽  
Toxicity Level

Terpenoids, including monoterpenoids (C10), norisoprenoids (C13) and sesquiterpenoids (C15), constitute a large group of plant-derived naturally occurring secondary metabolites which chemical structure is highly diverse. A quantitative structure-activity relationship (QSAR) model to predict the terpenoids toxicity and to evaluate the influences of their chemical structure, was developed in this study, by assessing the toxicity of 27 terpenoid standards using Gram-negative bioluminescent Vibrio fischeri, in real time. Under the test conditions, at concentration of 1 &micro;M, the terpenoids showed a toxicity level lower than five %, with exception of geraniol, citral, (S)-citronellal, geranic acid, (&plusmn;)-&alpha;-terpinyl acetate and geranyl acetone. Moreover, the standards tested displayed a toxicity level higher than 30 % at concentration of 50 to 100 &micro;M, with the exception of (+)-valencene, eucalyptol, (+)-borneol, guaiazulene, &beta;-caryophellene and linalool oxide. Regarding the functional group, the terpenoids toxicity was observed in the following order: alcohol &gt; aldehyde ~ ketone &gt; ester &gt; hydrocarbons. CODESSA software was employed to develop the QSAR models based on the correlation of terpenoids toxicity and a pool of descriptors related to each chemical structure. The QSAR models, based on t-test values, showed that terpenoids toxicity was mainly attributed to geometric (e.g., asphericity) and electronic (e.g., max partial charge for a C atom [Zefirov's PC]) descriptors. Statistically, the most significant overall correlation was the four-parameter equation with training and test coefficient correlation higher than 0.810 and 0.535, respectively, and square coefficient of cross-validation (Q2) higher than 0.689. According to the obtained data, the QSAR models are a suitable and a rapid tool to predict the terpenoids toxicity in a diversity of food products.

scholarly journals QSAR treatment of meisoindigo derivatives as a potentbreast anticancer agent

2019 ◽  
Vol 2 (2) ◽  
pp. 114 ◽  
Author(s):  
Evie Kama Lestari ◽  
Agus Dwi Ananto ◽  
Maulida Septiyana ◽  
Saprizal Hadisaputra
Keyword(s):  
Anticancer Activity ◽  
Anticancer Agent ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Multilinear Regression ◽  
Qsar Analysis ◽  
Pm3 Method ◽  
Structure Activity ◽  
Semiempirical Pm3 Method

A quantitative structure-activity relationship (QSAR) analysis of meisoindigo derivatives as a breast anticancer has been carried out. This study aimed to obtain the best QSAR model in order to design new meisoindigo based compounds with best anticancer activity. The semiempirical PM3 method was used for descriptor calculation. The best QSAR model was built using multilinear regression (MLR) with enter method. It was found that there were 19 new meisoindigo derivativeswith better predictive a potent anticancer agent. The best compound was (E)-2-(1-((3-ethylisoxazol-5-yl)methyl)-2-oxoindolin-3-ylidene)-N-(4-methoxyphenyl)acetamide with the value of IC505.31144 x10-15 (μM).

scholarly journals QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS (QSAR) OF VINCADIFFORMINE ANALOGUES AS THE ANTIPLASMODIAL COMPOUNDS OF THE CHLOROQUINOSENSIBLE STRAIN

2010 ◽  
Vol 5 (3) ◽  
pp. 255-260
Author(s):  
Iqmal Tahir ◽  
Mudasir Mudasir ◽  
Irza Yulistia ◽  
Mustofa Mustofa
Keyword(s):  
Molecular Orbital ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Activity Data ◽  
Qsar Analysis ◽  
Relationship Analysis ◽  
Structure Activity

Quantitative Structure-Activity Relationship (QSAR) analysis of vincadifformine analogs as an antimalarial drug has been conducted using atomic net charges (q), moment dipole (), LUMO (Lowest Unoccupied Molecular Orbital) and HOMO (Highest Occupied Molecular Orbital) energies, molecular mass (m) as well as surface area (A) as the predictors to their activity. Data of predictors are obtained from computational chemistry method using semi-empirical molecular orbital AM1 calculation. Antimalarial activities were taken as the activity of the drugs against chloroquine-sensitive Plasmodium falciparum (Nigerian Cell) strain and were presented as the value of ln(1/IC50) where IC50 is an effective concentration inhibiting 50% of the parasite growth. The best QSAR model has been determined by multiple linier regression analysis giving QSAR equation: Log (1/IC50) = 9.602.qC1 -17.012.qC2 +6.084.qC3 -19.758.qC5 -6.517.qC6 +2.746.qC7 -6.795.qN +6.59.qC8 -0.190. -0.974.ELUMO +0.515.EHOMO -0.274. +0.029.A -1.673 (n = 16; r = 0.995; SD = 0.099; F = 2.682)   Keywords: QSAR analysis, antimalaria, vincadifformine.  

Sign in / Sign up

Export Citation Format

Share Document