Capturing the crystal: Prediction of enthalpy of sublimation, crystal lattice energy, and melting points of organic compounds

Salahinejad, M, Le, T and Winkler, D 2013, 'Capturing the crystal: Prediction of enthalpy of sublimation, crystal lattice energy, and melting points of organic compounds', Journal of Chemical Information and Modeling, vol. 53, no. 1, pp. 223-229.


Document type: Journal Article
Collection: Journal Articles

Title Capturing the crystal: Prediction of enthalpy of sublimation, crystal lattice energy, and melting points of organic compounds
Author(s) Salahinejad, M
Le, T
Winkler, D
Year 2013
Journal name Journal of Chemical Information and Modeling
Volume number 53
Issue number 1
Start page 223
End page 229
Total pages 7
Publisher American Chemical Society
Abstract Accurate computational prediction of melting points and aqueous solubilities of organic compounds would be very useful but is notoriously difficult. Predicting the lattice energies of compounds is key to understanding and predicting their melting behavior and ultimately their solubility behavior. We report robust, predictive, quantitative structure-property relationship (QSPR) models for enthalpies of sublimation, crystal lattice energies, and melting points for a very large and structurally diverse set of small organic compounds. Sparse Bayesian feature selection and machine learning methods were employed to select the most relevant molecular descriptors for the model and to generate parsimonious quantitative models. The final enthalpy of sublimation model is a four-parameter multilinear equation that has an r2 value of 0.96 and an average absolute error of 7.9 ± 0.3 kJ.mol-1. The melting point model can predict this property with a standard error of 45 ± 1 K and r2 value of 0.79. Given the size and diversity of the training data, these conceptually transparent and accurate models can be used to predict sublimation enthalpy, lattice energy, and melting points of organic compounds in general.
Subject Cheminformatics and Quantitative Structure-Activity Relationships
Electroanalytical Chemistry
Theory and Design of Materials
Keyword(s) Structure-Property Relationship
Neural-Networks
Molecular Descriptors
Bayesian Methods
QSPR Models
QSAR
Solubility
Selection
Optimization
Solvation
DOI - identifier 10.1021/ci3005012
Copyright notice © 2012 American Chemical Society
ISSN 1549-9596
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