Prediction of Oral Acute Toxicity of Organophosphates Using QSAR Methods

2019 ◽  
Vol 16 ◽  
Author(s):  
Mina Kianpour ◽  
Esmat Mohammadinasab ◽  
Tahereh Momeni Esfahani
Keyword(s):  
Acute Toxicity ◽  
Molecular Descriptors ◽  
Back Propagation ◽  
Quantitative Structure Activity Relationship ◽  
Average Deviation ◽  
Qsar Models ◽  
Ann Models ◽  
Similar Class ◽  
New Compounds ◽  
Oral Acute Toxicity

: The aim of the present study was to develop quantitative structure-activity relationship (QSAR) models, based on molecular descriptors to predict the oral acute toxicity (LD50) of organophosphate compounds. The QSAR models based on genetic algorithm-multiple linear regression (GA-MLR) and back-propagation artificial neural network (BP-ANN) methods were proposed. The prediction experiment showed that the BP-ANN method was a reliable model for screening molecular descriptors, and molecular descriptors obtained by BP-ANN models could well characterize the molecular structure of each compound. It was indicated that among molecular descriptors to predict the LD50 (mgkg-1) of organophosphates, ALOGP2, RDF030u, RDF065p and GATS5m descriptors have more importance than the other descriptors. Also BP-ANN approach with the values of root mean square error (RMSE= 0.00168), square correlation coefficient (R2= 0.9999) and absolute average deviation (AAD=0.6981631) gave the best outcome, and the model predictions were in good agreement with experimental data. The proposed model may be useful for predicting LD50 (mgkg-1) of new compounds of similar class.

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 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.

scholarly journals Diterpenoid Lactones with Anti-Inflammatory Effects from the Aerial Parts of Andrographis paniculata

Molecules ◽  
2019 ◽  
Vol 24 (15) ◽  
pp. 2726 ◽  
Author(s):  
Lin Gan ◽  
Yuanru Zheng ◽  
Lijuan Deng ◽  
Pinghua Sun ◽  
Jiaxi Ye ◽  
...  
Keyword(s):  
Biological Activities ◽  
Quantitative Structure Activity Relationship ◽  
Rational Drug Design ◽  
Andrographis Paniculata ◽  
Aerial Parts ◽  
Rational Drug ◽  
Tnf Α ◽  
Qsar Models ◽  
Anti Inflammatory ◽  
New Compounds

Andrographis paniculata (AP) has been widely used in China for centuries to treat various diseases, and especially to treat inflammation. Diterpenoid lactones are the main anti-inflammatory components of AP. However, systematic chemical composition and biological activities, as well as key pharmacophores, of these diterpenoid lactones from AP have not yet been clearly understood. In this study, 17 diterpenoid lactones, including 2 new compounds, were identified by spectroscopic methods, and most of them attenuated the generation of TNF-α and IL-6 in LPS-induced RAW 274.7 cells examined by ELISA. Pharmacophores of diterpenoid lactones responsible for the anti-inflammatory activities were revealed based on the quantitative structure-activity relationship (QSAR) models. Moreover, new compounds (AP-1 and AP-4) exerted anti-inflammatory activity in LPS microinjection-induced zebrafish, which might be correlated with the inhibition of the translocation of NF-κB p65 from cytoplasm to nucleus. Our study provides guidelines for future structure modification and rational drug design of diterpenoid lactones with anti-inflammatory properties in medical chemistry.

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 QSAR Study of (5-Nitroheteroaryl-1,3,4-Thiadiazole-2-yl) Piperazinyl Derivatives to Predict New Similar Compounds as Antileishmanial Agents

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Abdellah Ousaa ◽  
Bouhya Elidrissi ◽  
Mounir Ghamali ◽  
Samir Chtita ◽  
Adnane Aouidate ◽  
...  
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
Leave One Out ◽  
New Compounds

To search for newer and potent antileishmanial drugs, a series of 36 compounds of 5-(5-nitroheteroaryl-2-yl)-1,3,4-thiadiazole derivatives were subjected to a quantitative structure-activity relationship (QSAR) analysis for studying, interpreting, and predicting activities and designing new compounds using several statistical tools. The multiple linear regression (MLR), nonlinear regression (RNLM), and artificial neural network (ANN) models were developed using 30 molecules having pIC50 ranging from 3.155 to 5.046. The best generated MLR, RNLM, and ANN models show conventional correlation coefficients R of 0.750, 0.782, and 0.967 as well as their leave-one-out cross-validation correlation coefficients RCV of 0.722, 0.744, and 0.720, respectively. The predictive ability of those models was evaluated by the external validation using a test set of 6 molecules with predicted correlation coefficients Rtest of 0.840, 0.850, and 0.802, respectively. The applicability domains of MLR and MNLR transparent models were investigated using William’s plot to detect outliers and outsides compounds. We expect that this study would be of great help in lead optimization for early drug discovery of new similar compounds.

scholarly journals QSAR investigation of acute toxicity of organic compounds during oral administration to mice

2019 ◽  
Vol 65 (2) ◽  
pp. 123-132 ◽  
Author(s):  
O.V. Tinkov ◽  
V.Yu. Grigorev ◽  
P.G. Polishchuk ◽  
A.V. Yarkov ◽  
O.A. Raevsky
Keyword(s):  
Acute Toxicity ◽  
Organic Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Mechanisms Of Action ◽  
Arithmetic Mean ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Structure Activity ◽  
Qsar Models ◽  
On Line

The effect of the structure of organic compounds on the acute toxicity upon oral injection in mice was studied using 2D simplex representation of the molecular structure and Random forest (RF) methods. Satisfactory quantitative structure-activity relationship (QSAR) models were constructed (R2 test = 0,61–0,62). The interpretation of the obtained QSAR models was carried out. The contributions of known toxicophores with established mechanisms of action were calculated in order to confirm the ability of the interpretation approach to correctly rank them relative to other structural fragments. The influence of the molecular surroundings of some toxicophores was analyzed. We analyzed the contributions of other highly ranked fragments from the list of common functional groups and ring systems in order to find new potential toxicophores. The on-line version of the expert system “OCHEM” (https://ochem.eu) and Arithmetic Mean Toxicity (AMT) approach were used for a comparative QSAR study.

scholarly journals Screening, Synthesis, and QSAR Research on Cinnamaldehyde-Amino Acid Schiff Base Compounds as Antibacterial Agents

Molecules ◽  
2018 ◽  
Vol 23 (11) ◽  
pp. 3027 ◽  
Author(s):  
Hui Wang ◽  
Mingyue Jiang ◽  
Fangli Sun ◽  
Shujun Li ◽  
Chung-Yun Hse ◽  
...  
Keyword(s):  
Antibacterial Activity ◽  
Schiff Base ◽  
Amino Acid ◽  
Qsar Model ◽  
Quantitative Structure Activity Relationship ◽  
New Drugs ◽  
Polarity Parameter ◽  
Qsar Models ◽  
New Compounds ◽  
Amino Acid Schiff Base

Development of new drugs is one of the solutions to fight against the existing antimicrobial resistance threat. Cinnamaldehyde-amino acid Schiff base compounds, are newly discovered compounds that exhibit good antibacterial activity against gram-positive and gram-negative bacteria. Quantitative structure–activity relationship (QSAR) methodology was applied to explore the correlation between antibacterial activity and compound structures. The two best QSAR models showed R2 = 0.9354, F = 57.96, and s2 = 0.0020 against Escherichia coli, and R2 = 0.8946, F = 33.94, and s2 = 0.0043 against Staphylococcus aureus. The model analysis showed that the antibacterial activity of cinnamaldehyde compounds was significantly affected by the polarity parameter/square distance and the minimum atomic state energy for an H atom. According to the best QSAR model, the screening, synthesis, and antibacterial activity of three cinnamaldehyde-amino acid Schiff compounds were reported. The experiment value of antibacterial activity demonstrated that the new compounds possessed excellent antibacterial activity that was comparable to that of ciprofloxacin.

DFT-Based QSAR Modelling of Inhibitory Activity of Coumarins and Sulfocoumarins on Carbonic Anhydrase (CA) Isoforms (CA I and CA II)

2019 ◽  
Vol 15 (3) ◽  
pp. 243-251 ◽  
Author(s):  
Erol Eroglu
Keyword(s):  
Molecular Descriptors ◽  
Binding Free Energy ◽  
Quantitative Structure Activity Relationship ◽  
Statistical Parameters ◽  
Final Model ◽  
Qsar Modelling ◽  
Qsar Models ◽  
Orbital Electron ◽  
Test Sets ◽  
Kinetics Of

<P>Objective: We present three robust, validated and statistically significant quantitative structure-activity relationship (QSAR) models, which deal with the calculated molecular descriptors and experimental inhibition constant (Ki) of 42 coumarin and sulfocoumarin derivatives measured against CA I and II isoforms. </P><P> Methods: The compounds were subjected to DFT calculations in order to obtain quantum chemical molecular descriptors. Multiple linear regression algorithms were applied to construct QSAR models. Separation of the compounds into training and test sets was accomplished using Kennard-Stone algorithm. Leverage approach was applied to determine Applicability Domain (AD) of the obtained models. </P><P> Results: Three models were developed. The first model, CAI_model1 comprises 30/11 training/test compounds with the statistical parameters of R2=0.85, Q2=0.77, F=27.57, R2 (test) =0.72. The second one, CAII_model2 comprises 30/12 training/test compounds with the statistical parameters of R2=0.86, Q2=0.78, F=30.27, R2 (test) =0.85. The final model, &#916;pKi_model3 consists of 25/3 training/ test compounds with the statistical parameters of R2=0.78, Q2=0.62, F=13.80 and R2(test) =0.99. </P><P> Conclusion: Interpretation of reactivity-related descriptors such as HOMO-1 and LUMO energies and visual inspection of their maps of orbital electron density leads to a conclusion that the binding free energy of the entire binding process may be modulated by the kinetics of the hydrolyzing step of coumarins.</P>

scholarly journals Application of in Silico Fragment and Data Analysis (ISIDA) Approaches for the Design of Novel Hydroxamic Acids Targeting HDAC2

2017 ◽  
Vol 33 (2) ◽  
Author(s):  
Pham The Hai ◽  
Dao Thi Kim Oanh ◽  
Doan Viet Nga ◽  
Bui Thanh Tung ◽  
Le Thi Thu Huong
Keyword(s):  
Rational Design ◽  
Molecular Descriptors ◽  
Model Development ◽  
Pharmaceutical Research ◽  
Quantitative Structure Activity Relationship ◽  
Inhibitory Potency ◽  
Histone Deacetylase 2 ◽  
Structure Activity ◽  
Qsar Models ◽  
New Treatment

Finding a new treatment for cancer is one of the most interested fields for pharmaceutical research worldwide. Enzyme histone deacetylase 2 (HDAC2), being a member of HDAC class I appear to be an important druggable target.This study focused on rational design of novel HDAC2 inhibitorsusing molecular descriptors derived from ISIDA fragmentor methodology. Quantitative structure-activity relationship was explored to develop mathematical models able to predict HDAC2 inhibitory bioactivity of acid hydroxamic derivatives. Multiple linear regression (MLR) algorithm implemented in STATISTICA 8.0 was used for model development. Consequently 3 QSAR models were obtained showing acceptable performance r2> 0.70 for further use. Based on these models 10 important fragments attributing to better inhibitory potency were identified. Finally several novel hydroxamic derivatives were designed and screened for HDAC2 inhibitory activity.

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