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Title: Hydrate crystal structures, radial distribution functions, and computing solubility
Author: Skyner, Rachael Elaine
ISNI:       0000 0004 6422 3596
Awarding Body: University of St Andrews
Current Institution: University of St Andrews
Date of Award: 2017
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Solubility prediction usually refers to prediction of the intrinsic aqueous solubility, which is the concentration of an unionised molecule in a saturated aqueous solution at thermodynamic equilibrium at a given temperature. Solubility is determined by structural and energetic components emanating from solid-phase structure and packing interactions, solute–solvent interactions, and structural reorganisation in solution. An overview of the most commonly used methods for solubility prediction is given in Chapter 1. In this thesis, we investigate various approaches to solubility prediction and solvation model development, based on informatics and incorporation of empirical and experimental data. These are of a knowledge-based nature, and specifically incorporate information from the Cambridge Structural Database (CSD). A common problem for solubility prediction is the computational cost associated with accurate models. This issue is usually addressed by use of machine learning and regression models, such as the General Solubility Equation (GSE). These types of models are investigated and discussed in Chapter 3, where we evaluate the reliability of the GSE for a set of structures covering a large area of chemical space. We find that molecular descriptors relating to specific atom or functional group counts in the solute molecule almost always appear in improved regression models. In accordance with the findings of Chapter 3, in Chapter 4 we investigate whether radial distribution functions (RDFs) calculated for atoms (defined according to their immediate chemical environment) with water from organic hydrate crystal structures may give a good indication of interactions applicable to the solution phase, and justify this by comparison of our own RDFs to neutron diffraction data for water and ice. We then apply our RDFs to the theory of the Reference Interaction Site Model (RISM) in Chapter 5, and produce novel models for the calculation of Hydration Free Energies (HFEs).
Supervisor: Mitchell, John B. O. Sponsor: Cambridge Crystallographic Data Centre (CCDC) ; Engineering and Physical Sciences Research Council (EPSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Chemistry ; Computational chemistry ; Solubility ; Informatics ; Regression ; RISM ; Reference Interaction Site Model ; PMF ; Potential of Mean Force ; Aqueous solubility ; Solubility prediction ; RDF ; Radial distribution function ; Organic hydrate ; Interactions ; Crystal structures ; CCDC ; CSD ; Cheminformatics ; Chemoinformatics ; Machine learning ; QD39.3E46S6 ; Chemistry--Data processing ; Hydrates--Structure ; Crystals--Structure