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.

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.

Predicting Impact Sensitivity of Heterocyclic Nitroarenes from Molecular Structures Selected by Genetic Algorithm

2012 ◽  
Vol 535-537 ◽  
pp. 2550-2553
Author(s):  
Rui Wang ◽  
Yong Gu Wang ◽  
Hui Liu
Keyword(s):  
Genetic Algorithm ◽  
Cross Validation ◽  
Correlation Coefficients ◽  
Molecular Structures ◽  
Impact Sensitivity ◽  
Optimal Subset ◽  
Leave One Out ◽  
The Impact ◽  
Prediction Capability ◽  
Good Agreement

A novel theoretical model was constructed to predict the impact sensitivity of 44 heterocyclic nitroarenes. The optimal subset of the molecular structures descriptors were selected by genetic algorithm (GA). The multiple linear regression (MLR) was then applied to build a prediction model of impact sensitivity for the 44 compounds. The correlation coefficients (R2) together with correlation coefficient of the leave-one-out cross validation (Q2CV) of the model is 0.928 and 0.865, respectively. The new model is highly statistically significant, and the robustness as well as internal prediction capability of which is satisfactory. The predicted impact sensitivity values are in good agreement with the experimental data.

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.

QSAR Studies of Halogenated Pyrimidine Derivatives as Inhibitors of Human Dihydroorotate Dehydrogenase Using Modified Bee Algorithm

2018 ◽  
Vol 21 (5) ◽  
pp. 381-387 ◽  
Author(s):  
Hossein Atabati ◽  
Kobra Zarei ◽  
Hamid Reza Zare-Mehrjardi
Keyword(s):  
External Validation ◽  
Correlation Coefficients ◽  
Quantitative Structure Activity Relationship ◽  
Molecular Structures ◽  
Dihydroorotate Dehydrogenase ◽  
Pyrimidine Derivatives ◽  
Qsar Studies ◽  
Bee Algorithm ◽  
Test Sets ◽  
Leave One Out

Aim and Objective: Human dihydroorotate dehydrogenase (DHODH) catalyzes the fourth stage of the biosynthesis of pyrimidines in cells. Hence it is important to identify suitable inhibitors of DHODH to prevent virus replication. In this study, a quantitative structure-activity relationship was performed to predict the activity of one group of newly synthesized halogenated pyrimidine derivatives as inhibitors of DHODH. Materials and Methods: Molecular structures of halogenated pyrimidine derivatives were drawn in the HyperChem and then molecular descriptors were calculated by DRAGON software. Finally, the most effective descriptors for 32 halogenated pyrimidine derivatives were selected using bee algorithm. Results: The selected descriptors using bee algorithm were applied for modeling. The mean relative error and correlation coefficient were obtained as 2.86% and 0.9627, respectively, while these amounts for the leave one out−cross validation method were calculated as 4.18% and 0.9297, respectively. The external validation was also conducted using two training and test sets. The correlation coefficients for the training and test sets were obtained as 0.9596 and 0.9185, respectively. Conclusion: The results of modeling of present work showed that bee algorithm has good performance for variable selection in QSAR studies and its results were better than the constructed model with the selected descriptors using the genetic algorithm method.

scholarly journals QSPR study on the gas/particle partition coefficient of PCBs by using molecular distance-edge vector index

2014 ◽  
Vol 79 (8) ◽  
pp. 965-975 ◽  
Author(s):  
Long Jiao ◽  
Xiaofei Wang ◽  
LI. Hua ◽  
Yunxia Wang
Keyword(s):  
Partition Coefficient ◽  
Cross Validation ◽  
External Validation ◽  
Quantitative Relationship ◽  
Structure Property ◽  
Prediction Ability ◽  
Vector Index ◽  
Artificial Neural Network Ann ◽  
Leave One Out ◽  
Edge Vector

The quantitative structure property relationship (QSPR) for gas/particle partition coefficient, Kp, of polychlorinated biphenyls (PCBs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PCBs. The quantitative relationship between the MDEV index and log Kp was modeled by multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave one out cross validation and external validation were carried out to assess the prediction ability of the developed models. When the MLR method is used, the root mean square relative error (RMSRE) of prediction for leave one out cross validation and external validation is 4.72 and 8.62 respectively. When the ANN method is employed, the prediction RMSRE of leave one out cross validation and external validation is 3.87 and 7.47 respectively. It is demonstrated that the developed models are practicable for predicting the Kp of PCBs. The MDEV index is shown to be quantitatively related to the Kp of PCBs.

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.

Prediction of glass transition temperatures of polyacrylates from the structures of motion units

2016 ◽  
Vol 15 (02) ◽  
pp. 1650011 ◽  
Author(s):  
Xinliang Yu ◽  
Xianwei Huang
Keyword(s):  
Glass Transition ◽  
Molecular Descriptors ◽  
Amorphous Polymer ◽  
External Validation ◽  
Data Sets ◽  
Structure Property ◽  
Internal Validation ◽  
The Stability ◽  
Leave One Out ◽  
Mlr Model

The glass transition temperature [Formula: see text] is the most important parameter of an amorphous polymer. A quantitative structure-property relationship (QSPR) was developed for [Formula: see text]s of 82 polyacrylates, by applying stepwise multiple linear regression (MLR) analysis. Molecular descriptors used to describe polymer structures were, for the first time, calculated from the motion units of polymer backbones, which are chain segments with 20 carbons in length (10 repeating units). After internal validation with leave-one-out (LOO) method, external validation was carried out to test the stability of the MLR model of [Formula: see text]s. Compared to the models already published in the literature, the MLR model in this paper was accurate and acceptable, although our model was based on bigger data sets. The feasibility of calculating molecular descriptors from the motion units of polymer backbones for developing [Formula: see text] models of polyacrylates has been demonstrated.

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 Structure and Physical Properties of Cardamonin: A Spectroscopic and Computational Approach

Molecules ◽  
2020 ◽  
Vol 25 (18) ◽  
pp. 4070 ◽  
Author(s):  
Iwona Budziak ◽  
Marta Arczewska ◽  
Daniel M. Kamiński
Keyword(s):  
Crystal Structure ◽  
Crystal Lattice ◽  
Density Functional ◽  
Xrd Analysis ◽  
Biological Properties ◽  
Molecular Structures ◽  
Functional Theory ◽  
Water Fractions ◽  
The Stability ◽  
Single Crystal Xrd Analysis

This is the first study of the crystal structure of cardamonin (CA) confirmed using single-crystal XRD analysis. In the crystal lattice of CA, two symmetry independent molecules are linked by hydrogen bonds within the layers and by the π···π stacking interactions in the columns which lead to the occurrence of two types of conformations among the CA molecules in the crystal structure. To better understand the stability of these arrangements in both crystals and the gaseous phase, seven different CA dimers were theoretically calculated. The molecular structures were optimized using density functional theory (DFT) at the B3LYP/6–311G+(d,p) level and the spectroscopic results were compared. It was found that the calculated configurations of dimer I and III were almost identical to the ones found in the CA crystal lattice. The calculated UV-Vis spectra for the CA monomer and dimer I were perfectly consistent with the experimental spectroscopic data. Furthermore, enhanced emissions induced by aggregated CA molecules were registered in the aqueous solution with the increase of water fractions. The obtained results will help to further understand the relation between a variety of conformations and the biological properties of CA, and the results are also promising in terms of the applicability of CA as a bioimaging probe to monitor biological processes.

APPLICATIONS OF A NEW TOPOLOGICAL INDEX EDm IN SOME ALIPHATIC HYDROCARBONS

2009 ◽  
Vol 08 (01) ◽  
pp. 19-45 ◽  
Author(s):  
CHANGMING NIE ◽  
YAXIN WU ◽  
RONGYAN WU ◽  
SAIHONG JIANG ◽  
CONGYI ZHOU
Keyword(s):  
Structural Information ◽  
Correlation Coefficients ◽  
Distance Matrix ◽  
Molar Refraction ◽  
Molecular Structures ◽  
Topological Descriptors ◽  
Standard Enthalpy Of Formation ◽  
Standard Entropy ◽  
Structure Property ◽  
Index Matrix

A novel index EDm based on ionicity index matrix, improved distance matrix, and branching degree matrix is used to describe the structural information of the molecule and realizes unique characterization of the molecular structures. The quantitative structure–property relationship (QSPR) models, with correlation coefficients (R) in the range of 0.99–1.00 for standard enthalpy of formation ([Formula: see text]), standard entropy ([Formula: see text]), molar volume (V m ), and molar refraction (R m ) of alkanes, alkenes, and alkynes, are subsequently developed using the index EDm. The leave-one-out (LOO) method and random sampling prediction (RSP) method demonstrate the models to be statistically significant and reliable. Compared with other published topological descriptors, the index EDm has many advantages such as zero degeneracy, better simulation, and so on. Furthermore, the models of solubility and octanol–water partition coefficient are built with satisfied results, which further manifests the superiority and wide application of this index.

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