Until such a time that computers are powerful enough to routinely perform ab initio simulation of large biomolecules, there will remain a demand for less expensive computational tools. Classical force field methods are widely used for the simulation of large molecules. However, their low computational cost comes at the price of introducing approximations to the description of the system, for example atomic point charges and Hooke type potentials. The quantum chemical topological force field, QCTFF, removes the classical approximations and uses a machine learning method, kriging, to build models that map ab initio atomic properties to changes in the internal coordinates of a chemical system. The atomic properties come from quantum chemical topology, QCT, and include atomic multipole moments and also energy terms from the interacting quantum atoms (IQA) energy decomposition scheme. By using atomic multipole moments, the electrostatic interactions between atoms is described in a more rigorous fashion than most classical force fields, and polarisation is captured through the use of kriging models. In this thesis, the QCTFF approach has been applied to a selection of test cases including small molecular dimers and amino acids. Kriging models are built using a “training set” of molecular geometries, and an investigation of different approaches for sampling amino acids is provided. The concept of the “atomic horizon sphere” is discussed, where the effect on the multipole moments of an atom in an increasingly large environment is investigated. This is an important investigation required to guide the development of future QCTFF training sets. Investigations into the effect of deprotonation of basic and acidic amino acids side chains is provided, as well as a study of the short range repulsion between atoms.