Despite achieving predictive performance, Notebook 5, employing regression tasks and Morgan fingerprints, allows limited room for improvements in terms of accuracy and interoperability. For this reason, DFT descriptors were calculated following the workflow shown Figure . This workflow takes the catalyst and substrate SMILES representations separately as input and generates 3D coordinates using RDKit. For each system, it performs conformational sampling (using GFN2-xTB (v.6.2) 126 ) to identify the lowest energy catalyst-CN complex. Unique structures are then selected based on an RMSD threshold of >1 Å; conformers, where CN was not accessible to the electrophiles, are excluded. For the remaining conformers, single-point energies are computed at the PBE-D3BJ/def2-SVP level of theory, identifying the lowest energy conformer. For this low energy conformer, five physical-chemical descriptors are extracted, including HOMO, LUMO, dipole moment, relevant atomic charges, Mayer bond orders, and NMR shielding tensors (Figure ). Additionally, steric descriptors for both catalysts and substrates are computed using wSterimol, 129 including B1 and B5 parameters representing the minimum and maximum widths of a substituent, and L parameter related to the length of the substituent (Figure ). In total, we employed 44 features, including 10 features for the substrates, 33 for the catalysts, and one for the temperature (Table ). Jupyter Notebook 7 is used for model training.