Hartree-Fock or post-Hartree-Fock, DFT) are highly accurate, they can be used effectively for catalyst design primarily in a post-hoc manner and on a limited number of systems. Alternatively, SEQM methods, although less accurate than the former, are significantly faster and can be envisioned as a useful tool in prospectively screening libraries of hundreds of potential catalysts due to their relatively low computational cost. However, the accuracy of SEQM in transition metal catalysis is yet to be demonstrated. In ML-based approaches, the information is usually represented by descriptors (also referred to as features). Different molecular representations exist (e.g., graph, simplified molecular-input line-entry system (SMILES)) and are linked with different molecular descriptors. Among those are system descriptors (temperature, concentration, etc.), steric descriptors (e.g., Sterimol parameters, average steric occupancy (ASO), %buried volume), electronic descriptors (e.g., Natural Bond Orbital (NBO)-charges, polarizability, Frontier Molecular Orbital (FMO)-gap), and geometric descriptors (e.g., bond lengths, dihedral angles). The latter are often obtained from QM calculations (see Box 2). Selection of descriptors and their computation. In TS-based models, descriptors are often chosen to describe the steric and electronic effects governing the reaction. It is important to note here that these descriptors generally have chemical meaning. For example, in both QM and MM, electronic descriptors such as atomic charges and dipole moments may be used to understand catalyst/ligand reactivity. Additionally, more advanced QM descriptors such as local and global reactivity parameters (obtainable in the cDFT framework -Box 1, see Supporting Information for detailed list of parameters), have often been used to rationalize the reactivity and selectivity of various chemical series in numerous reaction classes. In the case of statistical or machine learning models, thousands of descriptors may be computed and used for building the model, especially with the advent of specialized software for computing descriptors. However, without carefully choosing only the most important descriptors that significantly contribute to the prediction, the model is bound to contain a large amount of noise. This affects the accuracy of the predictions. Indeed, different methods to select descriptors (either supervised or unsupervised) have been developed to address this exact issue. These techniques (for a complete breakdown with examples see Supporting Information) include filtering methods (selection based on statistical methods like the chisquared test), wrapper methods (selection based on a predictive model to generate the best descriptor combinations), embedded methods (selection is made by learning the importance of each feature during model training), hybrid methods (combination of filtering and wrapper methods), and dimensionality reduction techniques (selection of features after dimensionality reduction of the data). Perhaps the most widespread method is principal component analysis (PCA), a technique that reduces data dimensions to fewer components while retaining essential information about its diversity. This allows for simpler visualization, although interpretation might not always be straightforward. If one of the goals is not only to develop a predictive model, but also to have chemically interpretable descriptors that can shed light on the mechanism (statistical models), then the descriptor selection requires additional attention. First, chemical knowledge may be used when selecting these. For example, if properties profoundly influencing the reaction outcome have been identified experimentally, descriptors of these properties may be considered. In this case, the ML or statistical model will eventually quantify the impact of these properties. Unfortunately, irrelevant descriptors may still coincidentally correlate with the property the model aims to predict, leading to poorly predicting models. Generally, the assumption is that the different descriptors are independent of each other (as in, the change of one will not influence the other). However, a counter example can be seen in the work of Werth et al. on bifunctional hydrogen bond donor (BHD) catalysts, where the NBO charge of the catalyst was indirectly correlated with the pKa value via the LUMO energy and a separate steric parameter.