Determination of boiling points of azeotropic mixtures using quantitative structure–property relationship (QSPR) strategy

Zare-Shahabadi, Vahid; Lotfi, Maryam; Gandomani, Abdol Rasoul Ahmadi; Papari, Mohammad Mehdi

doi:10.1016/j.molliq.2013.09.037

Cited by 17 publications

(5 citation statements)

References 45 publications

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“…Keeping in the mind that the calculated numerical descriptive vector for a peptide sequence is highly dependent on the AA indices used in the calculation, a proper set of AA indices was chosen by the Memorized_ACO algorithm − for each data set. The ACO algorithm is inspired by the behavior of real ants that are able to find the shortest path from a food to their nest.…”

Section: Computational Methodsmentioning

confidence: 99%

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

2020

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Predicting the bioactivity of peptides is an important challenge in drug development and peptide research. In this study, numerical descriptive vectors (NDVs) for peptide sequences were calculated based on the physicochemical properties of amino acids (AAs) and principal component analysis (PCA). The resulted NDV had the same length as the peptide sequence, so that each entry of NDV corresponded to one AA in the sequence. They were then applied to quantitative structure−activity relationship (QSAR) analysis of angiotensin-converting enzyme (ACE) inhibitor dipeptides, bitter-tasting dipeptides, and nonameric binding peptides of the human leukocyte antigens (HLA-A*0201). Multiple linear regression was used to construct the QSAR models. For each peptide set, a proper subset of physicochemical properties was chosen by the ant colony optimization algorithm. The leave-one-out cross-validation (q loo 2 ) values were 0.855, 0.936, and 0.642 and the root-mean-square errors (RMSEs) were 0.450, 0.149, and 0.461. Our results revealed that the new numerical descriptive vector can afford extensive characterization of peptide sequence so that it can be easily employed in peptide QSAR studies. Moreover, the proposed numerical descriptive vectors were able to determine hot spot residues in the peptides under study.

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Section: Computational Methodsmentioning

confidence: 99%

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

2020

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“…14,15 Computing of mixture descriptors is the most important problem facing in the QSPR study of mixtures. One solution to this problem is the combination of the molecular descriptors calculated for the individual constitutes via a specific mathematical formula, [16][17][18] Previously, we developed QSPR models of the prediction of normal boiling points of binary azeotropes. 19 22 different mixing rules were applied to compute mixture descriptors.…”

Section: Introductionmentioning

confidence: 99%

New structure-based models for the prediction of normal boiling point temperature of ternary azeotropes

Faramarzi

Abbasitabar

Jahromi

et al. 2021

JSCS

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Recently, development of the QSPR models for mixtures has received much attention. The QSPR modeling of mixtures requires the use of appropriate mixture descriptors. In this study, 12 mathematical equations were considered to compute mixture descriptors from the individual components for the prediction of normal boiling points of 78 ternary azeotropic mixtures. Multiple linear regression (MLR) was employed to build all QSPR models. Memorized_ACO algorithm was employed for subset variable selection. An ensemble model was also constructed using averaging strategy to improve the predictability of the final QSAR model. The models have been validated by a test set comprised of 24 ternary azeotropes and by different statistical tests. The resulted ensemble QSPR model had R2training, R2test, and q2 of 0.97, 0.95, and 0.96, respectively. Mean absolute error (MAE) as a good indicator of model performance were found to be 3.06 and 3.52 for training and testing sets, respectively.

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“…In addition, QSPR is considered to be a time-saving and effective method for prediction of the desired properties. In recent years, several QSPR models have been successfully developed to predict the physicochemical properties of mixtures, such as toxicity, boiling point, flash point, and critical parameters, all of which showed satisfactory stability and predictivity [9,10,11,12,13].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures

Shen

Pan

et al. 2019

IJMS

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A quantitative structure-property relationship (QSPR) study is performed to predict the auto-ignition temperatures (AITs) of binary liquid mixtures based on their molecular structures. The Simplex Representation of Molecular Structure (SiRMS) methodology was employed to describe the structure characteristics of a series of 132 binary miscible liquid mixtures. The most rigorous “compounds out” strategy was employed to divide the dataset into the training set and test set. The genetic algorithm (GA) combined with multiple linear regression (MLR) was used to select the best subset of SiRMS descriptors, which significantly contributes to the AITs of binary liquid mixtures. The result is a multilinear model with six parameters. Various strategies were employed to validate the developed model, and the results showed that the model has satisfactory robustness and predictivity. Furthermore, the applicability domain (AD) of the model was defined. The developed model could be considered as a new way to reliably predict the AITs of existing or new binary miscible liquid mixtures, belonging to its AD.

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Determination of boiling points of azeotropic mixtures using quantitative structure–property relationship (QSPR) strategy

Cited by 17 publications

References 45 publications

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling

New structure-based models for the prediction of normal boiling point temperature of ternary azeotropes

Prediction of the Auto-Ignition Temperatures of Binary Miscible Liquid Mixtures from Molecular Structures

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