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Aqueous solubility is a key parameter influencing the bioavailability of drugs and drug candidates. In this thesis computational models for the prediction of aqueous drug solubility were explored. High quality experimental solubility data for drugs were generated using a standardised protocol and models were developed using multivariate data analysis tools and calculated molecular descriptors. In addition, structural features associated with either solid-state limited or solvation limited solubility of drugs were identified.

Solvation, as represented by the octanol-water partition coefficient (logP), was found to be the dominant factor limiting the solubility of drugs, with solid-state properties being the second most important limiting factor.

The relationship between the chemical structure of drugs and the strength of their crystal lattice was studied for a dataset displaying logP-independent solubility. Large, rigid and flat molecules with an extended ring-structure and a large number of conjugated π-bonds were found to be more likely to have their solubility limited by a strong crystal lattice than were small, spherically shaped molecules with flexible side-chains.

Finally, the relationship between chemical structure and drug solvation was studied using computer simulated values of the free energy of hydration. Drugs exhibiting poor hydration were found to be large and flexible, to have low polarisability and few hydrogen bond acceptors and donors.

The relationship between the structural features of drugs and their aqueous solubility discussed in this thesis provide new rules-of-thumb that could guide decision-making in early drug discovery.