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.

A systematic comparison of the structural and dynamic properties of commonly used water models for molecular dynamics simulations

“…Finally, we used machine learning methods to explore the contributions of the parameters (model type, partial charge, Lennard-Jones parameters, bond length and angle) to the surface tension, dielectric constant, and self-diffusion coefficient. Machine learning can be used to predict a wide variety of material, [86][87][88][89] chemical, [90][91][92][93] and biological properties, [94][95][96][97] with several commercial and non-commercial open-source platforms that can be used to develop machine learning algorithms such as Schrödinger, 98 SYBYL, 99 TensorFlow (Google), 100 and BioPPSy. 101…”

Systematic Comparison of the Structural and Dynamic Properties of Commonly Used Water Models for Molecular Dynamics Simulations

“…Finally, we used machine learning methods to explore the contributions of the parameters (model type, partial charge, Lennard-Jones parameters, bond length, and angle) to the surface tension, dielectric constant, and self-diffusion coefficient. Machine learning can be used to predict a wide variety of material, − chemical, − and biological properties, − with several commercial and noncommercial open-source platforms that can be used to develop machine learning algorithms such as Schrödinger, SYBYL, TensorFlow (Google), and BioPPSy …”

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

“…In 2012, a state-of-the-art method was developed with multiple DNNs to predict the accuracy by 15% over the baseline RF method. Since it was implemented in the QSAR model, the RF-based QSAR method was often used in drug discovery approaches [ 161 ]. Recently, Zakharov et al developed a QSAR model combined with multitasking DNNs and consensus modelling to model the large-scale QSAR prediction for improved accuracy and better prediction over the concept of QSAR models [ 162 ].…”