Dataset Information

Predicting the Enthalpy and Gibbs Energy of Sublimation by QSPR Modeling.

ABSTRACT: The enthalpy and Gibbs energy of sublimation are predicted using quantitative structure property relationship (QSPR) models. In this study, we compare several approaches previously reported in the literature for predicting the enthalpy of sublimation. These models, which were reproduced successfully, exhibit high correlation coefficients, in the range 0.82 to 0.97. There are significantly fewer examples of QSPR models currently described in the literature that predict the Gibbs energy of sublimation; here we describe several models that build upon the previous models for predicting the enthalpy of sublimation. The most robust and predictive model constructed using multiple linear regression, with the fewest number of descriptors for estimating this property, was obtained with an R² of the training set of 0.71, an R² of the test set of 0.62, and a standard deviation of 9.1?kJ?mol^-1. This model could be improved by training using a neural network, yielding an R² of the training and test sets of 0.80 and 0.63, respectively, and a standard deviation of 8.9?kJ?mol^-1.

SUBMITTER: Meftahi N

PROVIDER: S-EPMC6021403 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Predicting the Enthalpy and Gibbs Energy of Sublimation by QSPR Modeling.

Meftahi Nastaran N Walker Michael L ML Enciso Marta M Smith Brian J BJ

Scientific reports 20180627 1

The enthalpy and Gibbs energy of sublimation are predicted using quantitative structure property relationship (QSPR) models. In this study, we compare several approaches previously reported in the literature for predicting the enthalpy of sublimation. These models, which were reproduced successfully, exhibit high correlation coefficients, in the range 0.82 to 0.97. There are significantly fewer examples of QSPR models currently described in the literature that predict the Gibbs energy of sublima ...[more]

PMID: 29950681

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Project description:BackgroundOne of the main topics in the development of quantitative structure-property relationship (QSPR) predictive models is the identification of the subset of variables that represent the structure of a molecule and which are predictors for a given property. There are several automated feature selection methods, ranging from backward, forward or stepwise procedures, to further elaborated methodologies such as evolutionary programming. The problem lies in selecting the minimum subset of descriptors that can predict a certain property with a good performance, computationally efficient and in a more robust way, since the presence of irrelevant or redundant features can cause poor generalization capacity. In this paper an alternative selection method, based on Random Forests to determine the variable importance is proposed in the context of QSPR regression problems, with an application to a manually curated dataset for predicting standard enthalpy of formation. The subsequent predictive models are trained with support vector machines introducing the variables sequentially from a ranked list based on the variable importance.ResultsThe model generalizes well even with a high dimensional dataset and in the presence of highly correlated variables. The feature selection step was shown to yield lower prediction errors with RMSE values 23% lower than without feature selection, albeit using only 6% of the total number of variables (89 from the original 1485). The proposed approach further compared favourably with other feature selection methods and dimension reduction of the feature space. The predictive model was selected using a 10-fold cross validation procedure and, after selection, it was validated with an independent set to assess its performance when applied to new data and the results were similar to the ones obtained for the training set, supporting the robustness of the proposed approach.ConclusionsThe proposed methodology seemingly improves the prediction performance of standard enthalpy of formation of hydrocarbons using a limited set of molecular descriptors, providing faster and more cost-effective calculation of descriptors by reducing their numbers, and providing a better understanding of the underlying relationship between the molecular structure represented by descriptors and the property of interest.

Dataset Information

Predicting the Enthalpy and Gibbs Energy of Sublimation by QSPR Modeling.

Publications

Predicting the Enthalpy and Gibbs Energy of Sublimation by QSPR Modeling.

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