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dc.contributor.advisorMitchell, John B. O.
dc.contributor.authorSkyner, Rachael Elaine
dc.coverage.spatial117en_US
dc.date.accessioned2017-09-26T15:23:17Z
dc.date.available2017-09-26T15:23:17Z
dc.date.issued2017-12
dc.identifieruk.bl.ethos.725083
dc.identifier.urihttp://hdl.handle.net/10023/11746
dc.description.abstractSolubility 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).en_US
dc.language.isoenen_US
dc.publisherUniversity of St Andrews
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectChemistryen_US
dc.subjectComputational chemistryen_US
dc.subjectSolubilityen_US
dc.subjectInformaticsen_US
dc.subjectRegressionen_US
dc.subjectRISMen_US
dc.subjectReference Interaction Site Modelen_US
dc.subjectPMFen_US
dc.subjectPotential of Mean Forceen_US
dc.subjectAqueous solubilityen_US
dc.subjectSolubility predictionen_US
dc.subjectRDFen_US
dc.subjectRadial distribution functionen_US
dc.subjectOrganic hydrateen_US
dc.subjectInteractionsen_US
dc.subjectCrystal structuresen_US
dc.subjectCCDCen_US
dc.subjectCSDen_US
dc.subjectCheminformaticsen_US
dc.subjectChemoinformaticsen_US
dc.subjectMachine learningen_US
dc.subject.lccQD39.3E46S6
dc.subject.lcshChemistry--Data processingen
dc.subject.lcshCheminformaticsen
dc.subject.lcshMachine learningen
dc.subject.lcshHydrates--Structureen
dc.subject.lcshCrystals--Structureen
dc.subject.lcshSolubilityen
dc.titleHydrate crystal structures, radial distribution functions, and computing solubilityen_US
dc.typeThesisen_US
dc.contributor.sponsorCambridge Crystallographic Data Centre (CCDC)en_US
dc.contributor.sponsorEngineering and Physical Sciences Research Council (EPSRC)en_US
dc.type.qualificationlevelDoctoralen_US
dc.type.qualificationnamePhD Doctor of Philosophyen_US
dc.publisher.institutionThe University of St Andrewsen_US


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