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.

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.

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.

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.

Chitosan-Nanocellulose Composites for Regenerative Medicine Applications

2020 ◽  
Vol 27 (28) ◽  
pp. 4584-4592 ◽  
Author(s):  
Avik Khan ◽  
Baobin Wang ◽  
Yonghao Ni
Keyword(s):  
Regenerative Medicine ◽  
Preparation Method ◽  
Chemical Properties ◽  
Biological Properties ◽  
Next Generation ◽  
Structure Property ◽  
Physico Chemical ◽  
Structure Property Relationship ◽  
Attractive Candidate ◽  
Fundamental Understanding

Regenerative medicine represents an emerging multidisciplinary field that brings together engineering methods and complexity of life sciences into a unified fundamental understanding of structure-property relationship in micro/nano environment to develop the next generation of scaffolds and hydrogels to restore or improve tissue functions. Chitosan has several unique physico-chemical properties that make it a highly desirable polysaccharide for various applications such as, biomedical, food, nutraceutical, agriculture, packaging, coating, etc. However, the utilization of chitosan in regenerative medicine is often limited due to its inadequate mechanical, barrier and thermal properties. Cellulosic nanomaterials (CNs), owing to their exceptional mechanical strength, ease of chemical modification, biocompatibility and favorable interaction with chitosan, represent an attractive candidate for the fabrication of chitosan/ CNs scaffolds and hydrogels. The unique mechanical and biological properties of the chitosan/CNs bio-nanocomposite make them a material of choice for the development of next generation bio-scaffolds and hydrogels for regenerative medicine applications. In this review, we have summarized the preparation method, mechanical properties, morphology, cytotoxicity/ biocompatibility of chitosan/CNs nanocomposites for regenerative medicine applications, which comprises tissue engineering and wound dressing applications.

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.

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