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.

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.

A systematic study of the structure–property relationship of a series of nonlinear optical (NLO) julolidinyl-based chromophores with a thieno[3,2-b]thiophene moiety

2015 ◽  
Vol 3 (2) ◽  
pp. 370-381 ◽  
Author(s):  
Airui Zhang ◽  
Hongyan Xiao ◽  
Shengyu Cong ◽  
Maolin Zhang ◽  
Hua Zhang ◽  
...  
Keyword(s):  
Systematic Study ◽  
Nonlinear Optical ◽  
Structure Property ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Thiophene Moiety ◽  
Nlo Chromophores ◽  
Relationship Of

A series of NLO chromophores a–d bearing thieno[3,2-b]thiophene (TT) as the conjugated bridge or the lateral moiety have been synthesized and investigated.

scholarly journals Quantitative Structure-Property Relationship Analysis Against Critical Micelle Concentration of Sulfonate-Based Surfactant Based on Semiempirical Zindo/1 Calculation

Molekul ◽  
2019 ◽  
Vol 14 (2) ◽  
pp. 78 ◽  
Author(s):  
Ponco Iswanto ◽  
Eva Vaulina Yulistia Delsy ◽  
Ely Setiawan ◽  
Fiandy Aminullah Putra
Keyword(s):  
Anionic Surfactant ◽  
Food Additives ◽  
Molecular Size ◽  
Quantitative Structure ◽  
Structure Property ◽  
Quantitative Structure Property Relationship ◽  
Relationship Analysis ◽  
Property Relationship ◽  
Structure Property Relationship ◽  
Independent Variable

Development of anionic surfactant compound isvery important because the anionic surfactant class iswidely used in people's lives. For instance,anionic surfactantsare used as food additives and detergents. The novelcompound of sulfonate-basedsurfactantor proposed compound has predictedthe CriticalMicelle Concentration(CMC) value of experiment. Quantitative Structure-Property Relationship (QSPR)analysisbased on semiempiricalZINDO/1 calculationwas conducted to obtain QSPR equation. Theoretical predictorsor independent variable which have an influence on the value of CMC are used to construct QSPR equation. The theoretical predictors areclassified intopredictor of electronic properties, solubility and steric. A total of 108experimentalCMCbelongs to sulfonate-basedsurfactant are calculated their theoretical predictors and analyzed by multiple linear regression. The QSPR equationwhich is obtainedfromthis study contains theimportant theoretical predictors.They are solubility properties, molecular weight, molecular size and net charge of carbon atomin thepolar partof sulfonate-based surfactant. This QSPR equation couldbe used to predict the CMC value of the novelsulfonate-based surfactant.

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.

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 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.

Exploration of Bis(Arylimidazole) Iridium Picolinate Complexes

2017 ◽  
Author(s):  
Aron Huckaba ◽  
sadig aghazada ◽  
iwan zimmermann ◽  
giulia grancini ◽  
natalia gasilova ◽  
...  
Keyword(s):  
Cyclic Voltammetry ◽  
Photophysical Properties ◽  
Structure Property ◽  
Aryl Group ◽  
Ligand Modification ◽  
Property Relationship ◽  
Structure Property Relationship

The straightforward synthesis and photophysical properties of a new series of heteroleptic Iridium (III) bis(2-arylimidazole) picolinate complexes is reported. Each complex has been characterized by NMR, UV-Vis, cyclic voltammetry, and the emissive properties of each is described. By systematically modifying first the cyclometallating aryl group on the arylimidazole ligand and then the picolinate ligand, the ramifications of ligand modification in these complexes was better understood through the construction of a structure-property relationship.

Exploration of Bis(Arylimidazole) Iridium Picolinate Complexes

2017 ◽  
Author(s):  
Aron Huckaba ◽  
sadig aghazada ◽  
iwan zimmermann ◽  
giulia grancini ◽  
natalia gasilova ◽  
...  
Keyword(s):  
Cyclic Voltammetry ◽  
Photophysical Properties ◽  
Structure Property ◽  
Aryl Group ◽  
Ligand Modification ◽  
Property Relationship ◽  
Structure Property Relationship

The straightforward synthesis and photophysical properties of a new series of heteroleptic Iridium (III) bis(2-arylimidazole) picolinate complexes is reported. Each complex has been characterized by NMR, UV-Vis, cyclic voltammetry, and the emissive properties of each is described. By systematically modifying first the cyclometallating aryl group on the arylimidazole ligand and then the picolinate ligand, the ramifications of ligand modification in these complexes was better understood through the construction of a structure-property relationship.

Quantitative Structure-Property Relationship (QSPR) Study of the Hydrophobicity of Phenols and 2-(Aryloxy-a-acetyl)-phenoxathiin Derivatives

2008 ◽  
Vol 59 (11) ◽  
Author(s):  
Adrian Beteringhe ◽  
Ana Cristina Radutiu ◽  
Titus Constantinescu ◽  
Luminita Patron ◽  
Alexandru T. Balaban
Keyword(s):  
Water Solubility ◽  
Molecular Descriptors ◽  
Log P ◽  
Structure Property ◽  
Substituted Phenols ◽  
Reverse Phase ◽  
Aromaticity Index ◽  
Structure Property Relationship ◽  
Layer Chromatography ◽  
Energy Of Formation

In a preceding study, the molecular hydrophobicity (RM0) was determined experimentally from reverse-phase thin-layer chromatography data for several substituted phenols and 2-(aryloxy-a-acetyl)-phenoxathiin derivatives, obtained from the corresponding phenoxides and 2-(a-bromoacetyl)-phenoxathiin. QSPR correlations for RM0 were explored using four calculated molecular descriptors: the water solubility parameter (log Sw), log P, the Gibbs energy of formation (DGf), and the aromaticity index (HOMA). Triparametric correlations do not improve substantially the biparametric correlation of RM0 in terms of log Sw and HOMA.

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