Structural Relationship Study of Octanol-Water Partition Coefficient of Some Sulfa Drugs Using GA-MLR and GA-ANN Methods

2020 ◽  
Vol 16 (3) ◽  
pp. 207-221
Author(s):  
Etratsadat Dadfar ◽  
Fatemeh Shafiei ◽  
Tahereh M. Isfahani
Keyword(s):  
Genetic Algorithm ◽  
Molecular Descriptors ◽  
Biological Activities ◽  
Structure Property ◽  
Ann Model ◽  
Sulfa Drugs ◽  
Data Set ◽  
Water Partition Coefficient ◽  
Wide Range ◽  
Comparison Of The Results

Aim and Objective: Sulfonamides (sulfa drugs) are compounds with a wide range of biological activities and they are the basis of several groups of drugs. Quantitative Structure-Property Relationship (QSPR) models are derived to predict the logarithm of water/ 1-octanol partition coefficients (logP) of sulfa drugs. Materials and Methods: A data set of 43 sulfa drugs was randomly divided into 3 groups: training, test and validation sets consisting of 70%, 15% and 15% of data point, respectively. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm - Multiple Linear Regressions (GA-MLR) and genetic algorithm -artificial neural network (GAANN) were employed to design the QSPR models. The possible molecular geometries of sulfa drugs were optimized at B3LYP/6-31G* level with Gaussian 98 software. The molecular descriptors derived from the Dragon software were used to build a predictive model for prediction logP of mentioned compounds. The Genetic Algorithm (GA) method was applied to select the most relevant molecular descriptors. Results: The R2 and MSE values of the MLR model were calculated to be 0.312 and 5.074 respectively. R2 coefficients were 0.9869, 0.9944 and 0.9601for the training, test and validation sets of the ANN model, respectively. Conclusion: Comparison of the results revealed that the application the GA-ANN method gave better results than GA-MLR method.

scholarly journals QSAR study of the octanol/water partition coefficient of organophosphorous compounds: The hybrid GA/MLR and GA/ANN approaches

2020 ◽  
Vol 85 (4) ◽  
pp. 467-480 ◽  
Author(s):  
Rana Amiri ◽  
Djelloul Messadi ◽  
Amel Bouakkadia
Keyword(s):  
Partition Coefficient ◽  
External Validation ◽  
Structure Property ◽  
Ann Model ◽  
Statistical Parameters ◽  
Qsar Study ◽  
Data Set ◽  
Relationship Analysis ◽  
Water Partition Coefficient ◽  
Variable Subset Selection

This study aimed at predicting the n-octanol/water partition coefficient (Kow) of 43 organophosphorous insecticides. Quantitative structure?property relationship analysis was performed on the series of 43 insecticides using two different methods, linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN), which Kow values of these chemicals to their structural descriptors. First, the data set was separated with a duplex algorithm into a training set (28 chemicals) and a test set (15 chemicals) for statistical external validation. A model with four descriptors was developed using as independent variables theoretical descriptors derived from Dragon software when applying genetic algorithm (GA)?variable subset selection (VSS) procedure. The values of statistical parameters, R2, Q2 ext, SDEPext and SDEC for the MLR (94.09 %, 92.43 %, 0.533 and 0.471, respectively) and ANN model (97.24 %, 92.17 %, 0.466 and 0.332, respectively) obtained for the three approaches are very similar, which confirmed that the employed four parameters model is stable, robust and significant.

QSAR Models for Predicting Aquatic Toxicity of Esters Using Genetic Algorithm-Multiple Linear Regression Methods

2019 ◽  
Vol 22 (5) ◽  
pp. 317-325
Author(s):  
Mehdi Rajabi ◽  
Fatemeh Shafiei
Keyword(s):  
Genetic Algorithm ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Molecular Descriptors ◽  
Biological Activities ◽  
Aquatic Toxicity ◽  
Qsar Model ◽  
Data Set ◽  
Test Set ◽  
Aliphatic Esters

Aim and Objective: Esters are of great importance in industry, medicine, and space studies. Therefore, studying the toxicity of esters is very important. In this research, a Quantitative Structure–Activity Relationship (QSAR) model was proposed for the prediction of aquatic toxicity (log 1/IGC50) of aliphatic esters towards Tetrahymena pyriformis using molecular descriptors. Materials and Methods: A data set of 48 aliphatic esters was separated into a training set of 34 compounds and a test set of 14 compounds. A large number of molecular descriptors were calculated with Dragon software. The Genetic Algorithm (GA) and Multiple Linear Regression (MLR) methods were used to select the suitable descriptors and to generate the correlation models that relate the chemical structural features to the biological activities. Results: The predictive powers of the MLR models are discussed by using Leave-One-Out (LOO) cross-validation and external test set. The best QSAR model is obtained with R2 value of 0.899, Q2 LOO =0.928, F=137.73, RMSE=0.263. Conclusion: The predictive ability of the GA-MLR model with two selected molecular descriptors is satisfactory and it can be used for designing similar group and predicting of toxicity (log 1/IGC50) of ester derivatives.

scholarly journals Artificial neural networks predictive models. A case study: carbon and bromine concentrations prediction based on chlorination time

2013 ◽  
Vol 14 (1) ◽  
pp. 10-17
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Potable Water ◽  
Back Propagation ◽  
Ann Model ◽  
Data Set ◽  
Test Set ◽  
Feed Forward Back Propagation ◽  
Artificial Neural ◽  
Comparison Of The Results

Artificial neural networks (ANNs) are being used increasingly to predict water variables. This study offers an alternative approach to quantify the relationship between time of chlorination in potable water (due to convectional treatment procedure) and chlorination by-products concentration (expressed as carbon and bromine) with an ANN model, i.e., capturing non-linear relationships among the water quality variables. Thus, carbon and bromine concentrations in potable water (the second chosen due to the toxicity of brominated trihalomethanes, THMs) were predicted using artificial neural networks (ANNs) based mainly on multi-layer perceptrons (MLPs) architecture. The chlorination (detention) time as much as 58 hours in Athens distributed network, comprised the input variables to the ANNs models. Moreover, to develop an ANN model for estimating carbon and bromine, the available data set was partitioned into training, validation and test set. In order to reach an optimum amount of hidden layers or nodes, different architectures were tested. The quality of the ANN simulations was evaluated in terms of the error in the validation sample set for the proper interpretation of the results. The calculated sum-squared errors for training, validation and test set were 0.056, 0.039 and 0.060 respectively for the best model selected. Comparison of the results showed that a two-layer feed-forward back propagation ANN model could be used as an acceptable model for predicting carbon and bromine contained in potable water THMs.

scholarly journals Optimizing the Rail Profile for High-Speed Railways Based on Artificial Neural Network and Genetic Algorithm Coupled Method

2020 ◽  
Vol 12 (2) ◽  
pp. 658
Author(s):  
Hanwen Jiang ◽  
Liang Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
High Speed ◽  
Sustainable Transportation ◽  
Ann Model ◽  
Coupled Method ◽  
Rail Wear ◽  
Wide Range ◽  
Artificial Neural

Though the high-speed railways are seen as a sustainable form of transportation, the fact that the rail wear in high-speed railways negatively affects the running safety and riding comfort, as well as the maintenance of railways, has drawn a wide range of concerns among researchers and scholars. In order to reduce the rail wear and achieve the goal of sustainable transportation, this paper proposes an ingenious optimization program of rail profiles based on the artificial neural network (ANN) and genetic algorithm (GA) coupled method. The candidate solutions of the nonlinear GA programming model are regarded as the inputs of the trained ANN model. Meanwhile, the outputs of the trained ANN model serve as the objective functions of the GA model. The computational results show that the optimized rail profile not only has superior performances in terms of the wheel/rail wear and contact conditions, but also maintains good dynamic performances. Therefore, this study can provide the theoretical and practical basis for the design and the preventive grinding of rails in the high-speed railways. Also, the ANN-GA coupled model can be extended and further employed on the optimization of other rail profiles.

Modeling and prediction of octanol/water partition coefficient of pesticides using QSPR methods

2017 ◽  
Vol 28 (4) ◽  
pp. 579-592 ◽  
Author(s):  
Amel Bouakkadia ◽  
Leila Lourici ◽  
Djelloul Messadi
Keyword(s):  
Partition Coefficient ◽  
External Validation ◽  
Selection Procedure ◽  
Support Vector ◽  
Structure Property ◽  
Statistical Validation ◽  
Data Set ◽  
Content Type ◽  
Relationship Analysis ◽  
Water Partition Coefficient

Purpose The purpose of this paper is to predict the octanol/water partition coefficient (Kow) of 43 organophosphorous compounds. Design/methodology/approach A quantitative structure-property relationship analysis was performed on a series of 43 pesticides using multiple linear regression and support vector machines methods, which correlate the octanol-water partition coefficient (Kow) values of these chemicals to their structural descriptors. At first, the data set was randomly separated into a training set (34 chemicals) and a test set (nine chemicals) for statistical external validation. Findings Models with three descriptors were developed using theoretical descriptors as independent variables derived from Dragon software while applying genetic algorithm-variable subset selection procedure. Originality/value The robustness and the predictive performance of the proposed linear model were verified using both internal and external statistical validation. One influential point which reinforces the model and an outlier were highlighted.

scholarly journals QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

2020 ◽  
Author(s):  
Ely Setiawan ◽  
Mudasir Mudasir ◽  
Karna Wijaya
Keyword(s):  
Machine Learning ◽  
Critical Micelle Concentration ◽  
Cationic Surfactants ◽  
Molecular Descriptors ◽  
Consensus Model ◽  
Structure Property ◽  
Learning Methods ◽  
Data Set ◽  
Chemical Modeling ◽  
Machine Learning Methods

<p> A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R<sup>2</sup> = 0.95, q<sup>2 </sup>= 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R<sup>2</sup> = 0.87, q<sup>2</sup> = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at https://ochem.eu/model/8425670 and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.</p>

Molecular descriptors of some chemicals that prevent COVID-19

2020 ◽  
Vol 17 ◽  
Author(s):  
Sourav Mondal ◽  
Nilanjan De ◽  
Anita Pal ◽  
Wei Gao
Keyword(s):  
Molecular Descriptors ◽  
Molecular Descriptor ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Property ◽  
Data Set ◽  
Discrimination Ability ◽  
Chemical Structures ◽  
Chemical Significance ◽  
Activity Modeling

Background: Topological index is numerical molecular descriptor that plays an important role in structureproperty/structure-activity modeling. A large number of works on multiplicative degree based indices has been developed. However, no attention is paid in investigating their chemical significance. Investigation of the chemical importance of such indices is needed. The computation of topological indices for different chemical structures and networks is a current topic of interest in mathematical chemistry. Objective: The objective of the present work is to examine the usefulness of the multiplicative degree based indices in quantitative structure property/activity relationship modeling. In addition, we intend to compute the indices for some antiCOVID-19 chemicals. Materials and Method: The regression analysis for octane data set is performed using MATLAB and Excel to check the predictability of the indices. The sensitivity test is conducted to examine the isomer discrimination ability. To study the indices for chemical structures preventing COVID-19, different combinatorial computation methods are utilized. Results and Discussion: The regression models governing the structural dependence of different properties and activities are derived. The supremacy of the indices as useful molecular descriptors compared to some well-known and most used descriptors is established. Explicit expressions of the indices for hydroxychloroquine, remdesivir (GS-5734) and theaflavin are obtained. Conclusion: As the indices are shown to have remarkable efficiency in quantitative structure property/activity relationship modeling and isomer discrimination, the outcomes can predict different properties and activities of the chemicals under consideration.

scholarly journals QSPR Models for Predicting Critical Micelle Concentration of Gemini Cationic Surfactants Combining Machine-Learning Methods and Molecular Descriptors

2020 ◽  
Author(s):  
Ely Setiawan ◽  
Mudasir Mudasir ◽  
Karna Wijaya
Keyword(s):  
Machine Learning ◽  
Critical Micelle Concentration ◽  
Cationic Surfactants ◽  
Molecular Descriptors ◽  
Consensus Model ◽  
Structure Property ◽  
Learning Methods ◽  
Data Set ◽  
Chemical Modeling ◽  
Machine Learning Methods

<p> A data set of 231 diverse gemini cationic surfactants has been developed to correlate the logarithm of critical micelle concentration (cmc) with the molecular structure using a quantitative structure-property relationship (QSPR) methods. The QSPR models were developed using the Online CHEmical Modeling environment (OCHEM). It provides several machine learning methods and molecular descriptors sets as a tool to build QSPR models. Molecular descriptors were calculated by eight different software packages including Dragon v6, OEstate and ALogPS, CDK, ISIDA Fragment, Chemaxon, Inductive Descriptor, SIRMS, and PyDescriptor. A total of 64 QSPR models were generated, and one consensus model developed by using a simple average of 13 top-ranked individual models. Based on the statistical coefficient of QSPR models, a consensus model was the best QSPR models. The model provided the highest R<sup>2</sup> = 0.95, q<sup>2 </sup>= 0.95, RMSE = 0.16 and MAE = 0.11 for training set, and R<sup>2</sup> = 0.87, q<sup>2</sup> = 0.87, RMSE = 0.35 and MAE = 0.21 for test set. The model was freely available at https://ochem.eu/model/8425670 and can be used for estimation of cmc of new gemini cationic surfactants compound at the early steps of gemini cationic surfactants development.</p>

Intrinsic Stacking Interactions of Natural and Artificial Nucleobases

2019 ◽  
Author(s):  
Drew P. Harding ◽  
Laura J. Kingsley ◽  
Glen Spraggon ◽  
Steven Wheeler
Keyword(s):  
Gas Phase ◽  
Electrostatic Interactions ◽  
Density Functional ◽  
Molecular Descriptors ◽  
Stacking Interactions ◽  
Interaction Energies ◽  
Functional Theory ◽  
Binding Partner ◽  
Heavy Atoms ◽  
Wide Range

The intrinsic (gas-phase) stacking energies of natural and artificial nucleobases were explored using density functional theory (DFT) and correlated ab initio methods. Ranking the stacking strength of natural nucleobase dimers revealed a preference in binding partner similar to that seen from experiments, namely G > C > A > T > U. Decomposition of these interaction energies using symmetry-adapted perturbation theory (SAPT) showed that these dispersion dominated interactions are modulated by electrostatics. Artificial nucleobases showed a similar stacking preference for natural nucleobases and were also modulated by electrostatic interactions. A robust predictive multivariate model was developed that quantitively predicts the maximum stacking interaction between natural and a wide range of artificial nucleobases using molecular descriptors based on computed electrostatic potentials (ESPs) and the number of heavy atoms. This model should find utility in designing artificial nucleobase analogs that exhibit stacking interactions comparable to those of natural nucleobases. Further analysis of the descriptors in this model unveil the origin of superior stacking abilities of certain nucleobases, including cytosine and guanine.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

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