Development of QSPR-ANN models for the estimation of critical properties of pure hydrocarbons

Fissa, Mohamed Roubehie; Lahiouel, Yasmina; Khaouane, Latifa; Hanini, Salah

doi:10.1016/j.jmgm.2023.108450

Cited by 5 publications

(1 citation statement)

References 61 publications

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“…Quantitative structure-property relationships (QSPR) is a technique that can predict the properties of chemical/biological systems based on their molecular structure. Relationships are often established using statistical modeling methods, such as artificial neural networks (ANN) Fissa et al, (2023).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of the effect of adsorption on the retention of organic compounds by NF/RO using QSPR-ANN

2024

Global NEST Journal

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<p>Understanding the retention of organic compounds (OCs) is critical for membrane applications in water recycling. The objective of this study was to create an optimized model using Artificial Neural Networks for Quantitative Structure-Property Relationship (QSPR-ANN) to predict the effect of adsorption on the retention of organic compounds (OCs) by nanofiltration (NF) and reverse osmosis (RO). An optimal model (QSPR-ANNoptimal) characterized by a similar structure (13 neurons in the inputs layer, 11 neurons in the hidden layer, and 1 neuron in the output layer) is constructed to predict the effect of adsorption on the retention of organic compounds by membranes. A set of 273 data points was used to test the neural network. the data set was used 70% for training, 15% for validation, and 15% for testing. For the most promising neural network model, the calculated retention values were compared to the experimental retention values, and good correlations were found (the determination coefficient "R2 = 0.9872" and the root mean squared error "RMSE = 2.2743%" for the test phase). This indicates the good robustness of the established QSPR-ANN model and the possibility of predicting the various parameters that characterize the retention of OCs by RO/NF. Sensitivity analysis revealed that the effect of adsorption retention of organic compounds by reverses osmosis and nanofiltration membranes depends more precisely on two important interactions (hydrophobic/adsorption and steric hindrance).</p>

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Section: Artificial Neural Networkmentioning

confidence: 99%

Prediction of the effect of adsorption on the retention of organic compounds by NF/RO using QSPR-ANN

2024

Global NEST Journal

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Predicting pKa of the carboxylic acid group in water solutions of amino acids based on molecular structures using machine learning QSPR methods

Fazeli,

Karimzadeh

2023

Materials Today Communications

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Predictive potential of distance-related spectral graphical descriptors for structure-property modeling of thermodynamic properties of polycyclic hydrocarbons with applications

Hayat,

Alanazi,

Imran

et al. 2024

Sci Rep

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A distance-related spectral descriptor is a graphical index with defining structure built on eigenvalues of chemical matrices relying on distances in graphs. This paper explores the predictive ability of both existing and new distance-related spectral descriptors for estimating thermodynamic characteristics of polycyclic hydrocarbons (PHs). As a standard choice, the entropy and heat capacity are selected to represent thermodynamic properties. Furthermore, 30 initial members of PHs are considered as test molecules for this study. Three new molecular matrices have been proposed and our research demonstrates that distance-spectral graphical indices built by these novel matrices surpass in efficiency relative to famous distance-spectral indices. First, a novel computational method is put forwarded to evaluate distance-spectral indices of molecular graphs. The proposed methodology is utilized to compute both pre-existing and novel distance-related spectral descriptors, with an aim to assess their predictive efficacy using experimental data pertaining to two selected thermodynamic properties. Subsequently, we identify the five most promising distance-related spectral descriptors, comprising the degree-distance and Harary energies, the recently introduced second geometric-arithmetic energy along with its associated Estrada invariant, and 2$$\text {nd}$$ atom-bond connectivity (ABC) Estrada index. Notably, the 2$$\text {nd}$$ ABC Estrada index and Harary energy demonstrate correlation coefficients exceeding 0.95, while certain conventional spectral indices including the distance energy as well as its associated Estrada index, display comparatively lower performance levels. Moreover, we illustrate the practical implications of our findings on specific classes of one-hexagonal nanocones and carbon polyhex nanotubes. These outcomes hold potential for enhancing the theoretical determination of certain thermodynamic attributes of these nanostructures, offering improved accuracy and minimal margin of error.

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Development of QSPR-ANN models for the estimation of critical properties of pure hydrocarbons

Cited by 5 publications

References 61 publications

Prediction of the effect of adsorption on the retention of organic compounds by NF/RO using QSPR-ANN

Prediction of the effect of adsorption on the retention of organic compounds by NF/RO using QSPR-ANN

Predicting pKa of the carboxylic acid group in water solutions of amino acids based on molecular structures using machine learning QSPR methods

Predictive potential of distance-related spectral graphical descriptors for structure-property modeling of thermodynamic properties of polycyclic hydrocarbons with applications

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