With respect to organocatalysis, NCIs are often a major factor in determining selectivities, which are hard to describe via standard molecular descriptors. Therefore, Orlandi et al. introduced computed NCI energies and distances between benzene and a probe residue as descriptors for NCIs (Figure ). The authors used these parameters in combination with other descriptors to model the enantioselectivities of a kinetic resolution of benzyl alcohols and an enantiodivergent fluorination of allylic alcohols, observing good correlations for both reactions. Since then, the proposed NCI descriptors have been successfully applied to multiple different reactions, such as an allenoate Claisen-rearrangement and a phase-transfer catalysed oxidative amination reaction . In the latter, NCI descriptors were both used to simplify previously existing MLR models and also led to a hypothesis of key NCIs in the transition state. Whereas these descriptors require the selection of a suitable probe model, Chen and coworkers recently proposed Pint as a descriptor of the London dispersion potential that is universal and can be calculated without a probe system . Although Pint has not been utilised for organocatalysis, the authors applied it to a Pd-metal catalysed enantioselective 1,1-diarylation of benzyl acrylates and found a similar performance compared to NCI probe descriptors. The open availability of larger, high-quality datasets also inspires other researchers to develop and apply ML algorithms and molecular representations. The previously described dataset from Denmark and coworkers has been adopted by other groups to develop and/or benchmark descriptors , models that use architectures designed to deal with multiple conformers (see Figure and also section 3.1) or models that are based on multiple fingerprints .