Another kind of representation that has found considerable application for ML in organocatalysis, is the use of descriptors. These are sets of chemical properties to characterise a molecule. A plethora of descriptors with varying degree of computational effort for their calculation are available. Among the most commonly employed descriptors in organocatalysis are steric and electronic descriptors. Section 3.1 provides a detailed overview of examples where different kind of descriptors have been successfully applied for predictive modelling in organocatalysis. In contrast to the representations through graphs, or SMILES, which can be directly obtained from the molecular structure, the selection of appropriate descriptors is problem-specific and requires knowledge about the fundamental interactions governing the reaction outcome. Hence, making the selection of input features a key step for successful modelling .