In MM, the molecules are somewhat represented as charged points (atoms) connected by springs (bonds) and interacting by other means (e.g., angles, torsions, van der Waals, electrostatic) in a 3-dimensional Cartesian space. Because MM does not consider electrons or nuclei, atoms (and the interactions between them) must be parametrized a priori. These parameters may be obtained either experimentally (e.g., van der Waals radius of an atom) or from high level QM calculations (e.g., atomic charge, force constants) and stored in lists termed force fields (FFs). Importantly, in MM, atoms in different chemical environments are distinguished by introducing the concept of atom types (i.e., an oxygen atom in water vs. in a carbonyl group would have different atom types), each bearing specific bonding and non-bonding parameters. It is thus important to define the correct atom type for a system under scrutiny, as the total energy of the system is calculated as a sum of contributions from both bonded and nonbonded terms. Due to the size of the chemical space ->10 small organic molecules -the number of possible atom types is virtually extremely large. To make the development of FFs tractable, the number of atom types is limited yet must be accurately assigned (together with the corresponding parameters) to each atom of any molecule. On the one hand, although the problem of accurately atom typing every molecule seems problematic, significant efforts have been made to exhaustively assign atom types to a large number of small molecules. On the other hand, others have proposed approaches discarding atom types altogether. Thus, the accuracy of MM calculations is strongly impacted by the choice of an FF and the assignment of atom types. QM. In contrast to MM, QM methods consider both electrons and nuclei. The orbitals required to calculate electronic terms are described by basis sets. Many different types of basis sets have been developed, and their usage depends on the system under scrutiny (i.e., metal complexes, organic molecules, etc.). As a rule of thumb, the larger the basis set, the more computationally expensive the simulations. The explicit treatment of electrons significantly increases the computational cost compared to MM, due to the resource-intensive computations of electronic integrals. Depending on the desired level of accuracy, significant speedups are possible, either through elegant algorithms for computing electronic integrals (see the ORCA SHARK integral engine ), lower-cost semi-empirical (SE) QM methods where some integrals are neglected (e.g., PM6, 94 GFN2-XTB ), composite methods such as HF-3c/PBEh-3c, or DFT. By far the most popular method is DFT, which has seen a tremendous uptake from organic chemists. Most often employed to rationalize reaction mechanisms, DFT has more recently been used to try and explain the reactivity of different types of chemicals (through the conceptual DFT -cDFT -framework). Higher accuracy algorithms such as Møller-Plesset (MP) or coupled cluster (CC) are generally reserved for advanced theoretical work and are not commonly employed in routine organic chemistry simulations. Overall, the accuracy of the calculations is highly dependent on the choice of method (e.g., MP2, DFT, SE) and basis sets. ML. ML methods differ significantly from both MM and QM. ML algorithms (and the more interpretable statistical models) aim at finding and quantifying patterns in vast amounts of data for predicting a given outcome. In chemistry, this outcome can come in the form of a single number (e.g., pKa, solubility, enantioselectivity), while the data can be in the form of structural data, reaction data, etc. As a major advantage, ML algorithms are orders of magnitude faster than both MM and QM. In contrast to both QM and MM, most ML models are "black box", meaning that the way in which an algorithm arrives at a prediction is unknown, although explainable artificial intelligence (AI) is emerging. There are multiple flavours of ML: supervised, unsupervised, and reinforcement, each with their own advantages and disadvantages. For example, supervised learning requires labelled input and output training data (e.g., catalyst A provides an enantioselectivity of 45 %ee), whereas unsupervised learning deals with raw data. In reinforcement learning, a feedback loop is employed so that the model can learn from its environment and maximize its correct predictions. Irrespective of flavour or model, the key ingredients for a predictive ML model are data quality and quantity.