For the description of intramolecular energetics, quantum mechanics (QM) is seemingly preferable and is frequently used in computational enzymology applications. However, the computational cost associated with QM simulations is high, particularly for free energy predictions which require extensive (alchemical and/or conformational) sampling. In order to make a calculation tractable the level of QM theory (basis set and exchange-correlation functional, for example) is often compromised, which again raises questions over the final accuracy. Alternatively, one can construct direct fits to the high dimensional QM potential energy surface of the molecule. There is a wide range of methods available for fitting bond, angle and dihedral parameters of the MM force field to QM energies, gradients and Hessian matrices, and these often include extended functional forms such as cross-terms to account for coupling between internal coordinates. However, for larger, more flexible molecules, longer-range atomic interactions beyond the 1-4 interaction are also crucial in determining molecular conformation. For these molecules, a consistent, accurate approach to approximating the QM potential energy surface is key. Rather than relying on human intuition to decide on the most appropriate functional form this MM force field should take, it is preferable to harness the recent advances of machine learning inspired techniques. There are several neural network and kernel based techniques recently developed for material systems that can predict quantum energies and forces with remarkable accuracy. Since these potentials need to be trained on only a few thousand (well dispersed) configurations, the underlying quantum mechanical data can be of high accuracy while maintaining affordable computational expense. These techniques have been successfully used to reproduce the atomisation energies and QM potential energy surfaces 40-42 of a range of organic molecules.