scholarly journals An Effective Prediction of Biomagnification Factors for Organochlorine Pollutants

2021 ◽  
Author(s):  
Ming Cai Zhang ◽  
Hong Lin Zhai ◽  
Ke Xin Bi ◽  
Bin Qiang Zhao ◽  
Hai Ping Shao
Keyword(s):  
Correlation Coefficient ◽  
Predictive Ability ◽  
Structure Property ◽  
Organochlorine Pollutants ◽  
Structure Property Relationship ◽  
Tchebichef Moments ◽  
Independent Test ◽  
Molecular Information ◽  
Leave One Out ◽  
Multivariate Image

Abstract Biomagnification factor (BMF) is an important index of pollutants in food chains but its experimental determination is quite tedious. In this contribution, as the feature descriptors of molecular information, Tchebichef moments (TMs) were calculated from their structural images. Then stepwise regression was employed to establish the prediction model for the logBMF of organochlorine pollutants. The correlation coefficient with leave-one-out cross-validation (Rcv) was 0.9570 and the correlation coefficient of prediction (Rp) for external independent test set was 0.9594. Compared with traditional two-dimensional (2D) quantitative structure-property relationship (QSPR) and the reported augmented multivariate image analysis applied to QSPR (aug-MIA-QSPR), the proposed approach is more simple, accurate and reliable. This study not only obtained the model with better stability and predictive ability for the BMF of organochlorine pollutants, but also provided another effective approach to QSPR research.

scholarly journals An effective prediction of biomagnification factors for organochlorine pollutants

2021 ◽  
Author(s):  
Ming Cai Zhang ◽  
Hong Lin Zhai ◽  
Ke Xin Bi ◽  
Bin Qiang Zhao ◽  
Hai Ping Shao
Keyword(s):  
Correlation Coefficient ◽  
Predictive Ability ◽  
Structure Property ◽  
Organochlorine Pollutants ◽  
Structure Property Relationship ◽  
Tchebichef Moments ◽  
Independent Test ◽  
Molecular Information ◽  
Leave One Out ◽  
Multivariate Image

Abstract Biomagnification factor (BMF) is an important index of pollutants in food chains but its experimental determination is quite tedious. In this contribution, as the feature descriptors of molecular information, Tchebichef moments (TMs) were calculated from their structural images. Then stepwise regression was employed to establish the prediction model for the logBMF of organochlorine pollutants. The correlation coefficient with leave-one-out cross-validation (Rcv) was 0.9570; the correlation coefficient of prediction (Rp) and root mean square error (RMSEp) for external independent test set reached 0.9594 and 0.2129, respectively. Compared with traditional two-dimensional (2D) quantitative structure-property relationship (QSPR) and the reported augmented multivariate image analysis applied to QSPR (aug-MIA-QSPR), the proposed approach is more simple, accurate and reliable. This study not only obtained the model with better stability and predictive ability for the BMF of organochlorine pollutants, but also provided another effective approach to QSPR research.

scholarly journals A Novel Approach to the Prediction of Biomagnification Factors Based on Molecular Structure Images

2021 ◽  
Author(s):  
Ming Cai Zhang ◽  
Ling Zhu ◽  
Hong Lin Zhai ◽  
Ke Xin Bi ◽  
Bing Qiang Zhao
Keyword(s):  
Prediction Model ◽  
Structure Property ◽  
Organochlorine Pollutants ◽  
Novel Approach ◽  
Structure Property Relationship ◽  
Tchebichef Moments ◽  
Independent Test ◽  
Feature Information ◽  
Leave One Out ◽  
Satisfactory Prediction

Abstract Although biomagnification factor (BMF) is an important index of pollutants in food chains, its experimental determination is quite tedious. In this contribution, as the feature information, Tchebichef moments (TMs) were calculated directly from the molecular structural images, and then stepwise regression was employed to establish the prediction model of the logBMF. The proposed approach was applied to the logBMF prediction of organochlorine pollutants, and the correlation coefficient with leave-one-out cross-validation (Rcv) of the obtained model was 0.96, and the root mean square error (RMSEp) for the external independent test set was 0.21. Compared with traditional two-dimensional (2D) quantitative structure-property relationship (QSPR) as well as the reported method, the proposed approach was more simple, accurate and reliable. This study not only obtained the satisfactory prediction model for organochlorine pollutants, but also provided another effective approach to QSPR research.

Quantitative Structure- Property Relationship (QSPR) Investigation of Camptothecin Drugs Derivatives

2018 ◽  
Vol 21 (7) ◽  
pp. 533-542 ◽  
Author(s):  
Neda Ahmadinejad ◽  
Fatemeh Shafiei ◽  
Tahereh Momeni Isfahani
Keyword(s):  
Thermodynamic Properties ◽  
Predictive Ability ◽  
Total Sample ◽  
Basis Sets ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Chemical Structures ◽  
Property Relationship ◽  
Structure Property Relationship

Aim and Objective: Quantitative Structure- Property Relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules to their known properties. In this study, QSPR models have been developed for modeling and predicting thermodynamic properties of 76 camptothecin derivatives using molecular descriptors. Materials and Methods: Thermodynamic properties of camptothecin such as the thermal energy, entropy and heat capacity were calculated at Hartree–Fock level of theory and 3-21G basis sets by Gaussian 09. Results: The appropriate descriptors for the studied properties are computed and optimized by the genetic algorithms (GA) and multiple linear regressions (MLR) method among the descriptors derived from the Dragon software. Leave-One-Out Cross-Validation (LOOCV) is used to evaluate predictive models by partitioning the total sample into training and test sets. Conclusion: The predictive ability of the models was found to be satisfactory and could be used for predicting thermodynamic properties of camptothecin derivatives.

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.

Exploratory and machine learning analysis of the stability constants of HgII- triazene ligands complexes

2021 ◽  
pp. 1-13
Author(s):  
Ahmadreza Hajihosseinloo ◽  
Maryam Salahinejad ◽  
Mohammad Kazem Rofouei ◽  
Jahan B. Ghasemi
Keyword(s):  
Stability Constants ◽  
Goodness Of Fit ◽  
Predictive Ability ◽  
Support Vector ◽  
Structure Property ◽  
Charge Effects ◽  
Proposed Model ◽  
The Stability ◽  
Leave One Out ◽  
External Test

Knowing stability constants for the complexes HgII with extracting ligands is very important from environmental and therapeutic standpoints. Since the selectivity of ligands can be stated by the stability constants of cation–ligand complexes, quantitative structure–property relationship (QSPR) investigations on binding constant of HgII complexes were done. Experimental data of the stability constants in ML2 complexation of HgII and synthesized triazene ligands were used to construct and develop QSPR models. Support vector machine (SVM) and multiple linear regression (MLR) have been employed to create the QSPR models. The final model showed squared correlation coefficient of 0.917 and the standard error of calibration (SEC) value of 0.141 log K units. The proposed model presented accurate prediction with the Leave-One-Out cross validation ( Q LOO 2  = 0.756) and validated using Y-randomization and external test set. Statistical results demonstrated that the proposed models had suitable goodness of fit, predictive ability, and robustness. The results revealed the importance of charge effects and topological properties of ligand in HgII - triazene complexation.

Intelligent Consensus Predictions of Biodegradation Half-Life of Petroleum Hydrocarbons (PHCs)

2021 ◽  
Vol 6 (3) ◽  
pp. 1-24
Author(s):  
Sulekha Ghosh ◽  
Probir Kumar Ojha
Keyword(s):  
Half Life ◽  
Petroleum Hydrocarbons ◽  
Predictive Ability ◽  
Molecular Size ◽  
Absolute Error ◽  
Structure Property ◽  
Degree Of Unsaturation ◽  
Validation Parameters ◽  
Structure Property Relationship ◽  
Thiophene Moiety

The present study explores the important chemical features of diverse petroleum hydrocarbons (PHCs) responsible for their biodegradation by developing partial least squares (PLS) regression-based quantitative structure-property relationship (QSPR) models. The biodegradability is estimated in terms of biodegradation half-life (Logt1/2). All the PLS models were extensively validated by different internationally acceptable internal (R2= 0.849–0.861; Q2 = 0.833–0.849; R2adj = 0.845–0.858) and external (Q2F1= 0.825-0.848; Q2F2 = 0.822–0.845) validation parameters. The consensus predictions were also performed by using the “intelligent consensus predictor” (ICP) tool, which improves the predictive ability of individual models based on mean absolute error (MAE)-based criteria. The models suggested that the biodegradation of PHCs is dependent on the presence of substituents on the aromatic ring, 12 atom containing ring system, thiophene moiety, electron rich chemicals, large molecular size, degree of unsaturation, degree of branching, cyclization, and hydrophobicity.

QSPR Study on Electric Spark Sensitivity of Nitro Arenes

2011 ◽  
Vol 284-286 ◽  
pp. 197-200 ◽  
Author(s):  
Rui Wang ◽  
Jun Cheng Jiang ◽  
Yong Pan
Keyword(s):  
Function Approximation ◽  
Significant Contribution ◽  
Correlation Coefficients ◽  
Structure Property ◽  
Qspr Model ◽  
Structure Property Relationship ◽  
Leave One Out ◽  
The Relationship ◽  
Prediction Capability ◽  
Good Agreement

A quantitative structure-property relationship (QSPR) model was proposed for predicting electric spark sensitivity of 39 nitro arenes. The genetic function approximation (GFA) was employed to select the descriptors that have significant contribution to electric spark sensitivity from various descriptors and for fitting the relationship existed between the selected 8 descriptors and electric spark sensitivity. The correlation coefficients (R2) together with correlation coefficient of the leave-one-out cross validation (Q2CV) of the model are 0.924 and 0.873, respectively. The model is highly statistically significant, and the robustness as well as internal prediction capability of which is satisfactory. The results show that the predicted electric spark sensitivity values are in good agreement with the experimental data.

QSPR/QSAR Study of Mercaptans by Quantum Topological Method

2011 ◽  
Vol 233-235 ◽  
pp. 2536-2540
Author(s):  
Xuan Chen ◽  
Chang Ming Nie ◽  
Song Nian Wen
Keyword(s):  
Density Functional ◽  
Cross Validation ◽  
Correlation Coefficients ◽  
Predictive Ability ◽  
Molecular Structures ◽  
Structure Property ◽  
Qsar Study ◽  
Validation Parameters ◽  
The Stability ◽  
Leave One Out

A new molecular quantum topological index QT was constructed by molecular topological methods and quantum mechanics (QM), which together with Gibbs free energy(G), Constant volume mole hot melting(CV) that were calculated by density functional theory (DFT) at the B3LYP/6-31G(d) level of theory for mercaptans. Index QT can not only efficiently distinguish molecular structures of mercaptans, but also possess good applications of QSPR/QSAR (quantitative structure-property/activity relationships). And most of the correlation coefficients of the models were over 0.99. The LOO CV (leave-one-out cross-validation) method was used to testify the stability and predictive ability of the models. The validation results verified the good stability and predictive ability of the models employing the cross-validation parameters: RCV, SCVand FCV, which demonstrated the wide potential of the index QT for applications to QSPR/ QSAR.

scholarly journals A Refractive Index Study of a Diverse Set of Polymeric Materials by QSPR with Quantum-Chemical and Additive Descriptors

Molecules ◽  
2020 ◽  
Vol 25 (17) ◽  
pp. 3772
Author(s):  
Meade E. Erickson ◽  
Marvellous Ngongang ◽  
Bakhtiyor Rasulev
Keyword(s):  
Refractive Index ◽  
Predictive Ability ◽  
Polymeric Materials ◽  
Predictive Performance ◽  
Structure Property ◽  
Variable Model ◽  
Qspr Model ◽  
Index Study ◽  
Leave One Out ◽  
2D And 3D

Predicting the activities and properties of materials via in silico methods has been shown to be a cost- and time-effective way of aiding chemists in synthesizing materials with desired properties. Refractive index (n) is one of the most important defining characteristics of an optical material. Presented in this work is a quantitative structure–property relationship (QSPR) model that was developed to predict the refractive index for a diverse set of polymers. A number of models were created, where a four-variable model showed the best predictive performance with R2 = 0.904 and Q2LOO = 0.897. The robustness and predictability of the best model was validated using the leave-one-out technique, external set and y-scrambling methods. The predictive ability of the model was confirmed with the external set, showing the R2ext = 0.880. For the refractive index, the ionization potential, polarizability, 2D and 3D geometrical descriptors were the most influential properties. The developed model was transparent and mechanistically explainable and can be used in the prediction of the refractive index for new and untested polymers.

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.

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