Partition coefficient and solubility are very useful properties in a variety of product and process
design problems. Especially in the octanol-water system, partition coefficients (Ki_OW) are
used as indicators for a drug's lipophilicity which is a key physicochemical property in drug
design. Solid phase solubility is a fundamental parameter in the design of crystallisation processes
commonly used in the pharmaceutical and agrochemical industries. The ability to predict
these properties from the molecular structure of compounds is therefore highly desirable. In this
thesis, the recently developed SAFT-γ Mie group-contribution (GC) equation of state is used as
a predictive framework to study the thermodynamic properties of multifunctional compounds.
The SAFT-γ Mie approach allows one to determine the thermo-physical properties of molecules
in terms of the constituent functional groups that represent their unique molecular identity. The
parameters for each functional group are developed from fluid-phase equilibrium data for simple compounds and, once estimated, they are applied to the study of more complex molecules in
a predictive manner. Novel SAFT-γ models are developed for fundamental systems such as
alkane + water and alcohol + water mixtures, which are typically involved in various chemical
and biological applications. These GC models are able to describe accurately mutual solubilities
of water and hydrocarbons which span more than ten orders of magnitude, and are also transferable
to the modelling of multifunctional compounds. As a result, a quantitative prediction of
Ki_OW and solubility is achieved for several active pharmaceutical ingredients (API) including
ibuprofen, ketoprofen, lovastatin, and simvastatin. We find that an important factor that needs
to be taken into account in modelling these complex APIs is the formation of intramolecular
hydrogen bonds (IMHB). IMHB have a pronounced effect on molecular structure and thermodynamic
properties, but are often overlooked by other predictive approaches. Modelling complex
organic molecules with the consideration of IMHB is challenging, especially for GC approaches
which do not take into account details of molecular conformation. In this thesis, an effective
treatment for IMHB is developed within the SAFT-γ
Mie framework and proven to improve the
property prediction of molecules with IMHB, especially in highly associated solvents. The findings in this thesis validate the applicability of the SAFT-γ Mie approach in modelling complex multifunctional molecules and demonstrate its broad relevance for the pharmaceutical industry.