The field of computational chemistry is constantly developing new predictive methods to guide experiments. Co-crystals are a promising development for improving the properties of active molecules, which is an exciting prospect specifically for the pharmaceutical field in formulating active pharmaceutical ingredients (API). To minimise computational cost, quantum mechanical models based on density functional theory (DFT) can be supported where appropriate with faster semi-empirical density functional tight binding (DFTB). These types of models can be used to predict the enthalpy of formation of the co-crystal structures. My results show that the full DFT methodologies predict the enthalpy of formation well for a broad range of co-crystals, with the DFTB methods giving high-throughput predictions for simple co-crystals but failing for larger, more complex APIs. Another area of intensive research in multi-component pharmaceutical forms is the development of anti-cancer artificial metallonucleases (AMNs) that can be used to recognise and damage nucleic acids. This is aided by the metal centre which promotes oxidative processes chiefly responsible for cleavage activity. Thus, ensuring coordination of the metals in their parent AMN scaffolds is imperative for them to function correctly. Click chemistry is a recently discovered modular process to produce various AMNs with differing terminal groups. The goal here is to produce various polynuclear AMN structures, as these have previously been found to have a greater activity than mononuclear congeners, resulting in greater DNA damaging effects. This thesis aims to predict metal ion binding properties based on a wide range of scaffolds. The pendant groups can influence the strength of the metal to scaffold binding. DFT calculations are used to predict the effect of a broad range of molecular groups in place of the hetero-aromatic donors and explore how this can improve the metal binding in molecular scaffolds.