Parametrização de ligantes em reações catalisadas por metais de transição ; Parameterization of ligands in reactions catalyzed by transition metals

The approach of statistical methods capable of accurately predicting the relationship between catalyst structure and reactivity represents a major impact on reaction development, since both reactivity and selectivity of the catalyst employed in organic reactions are directly related to its structural aspects. In the case of transition metal catalysis, this parameterization provides quick information and relevant estimates about the structure and respective activity of new ligands that are briefly identified in structural descriptors that influence the desired activity. Here we list key results obtained in the development and use of ligand parameterization to guide, predict and explain the discovery and development of new catalysts and methodologies. In the context of nucleopalladation reactions, we highlight Wacker-type reactions that employ carbonylation reactions of double bonds not activated by CO capture in a palladium-catalyzed process. We present a new methodology to obtain pyrazoline ester derivatives tolerant to different substitutions at strategic positions in the starting material as demonstrated by the scope study. By statistical studies, it was possible to observe that the molecular softness (ƞ) of the pyridine has a direct relationship with the yield - except for the bulkiest pyridines. For monophosphine ligands, the reactivity threshold analysis revealed that bulkier ligands are associated with the worst results. When bisphosphines are involved, the angle between the phosphorus substituents in the complex with PdCl2 indicates an important role, which can be interpreted in terms of flexibility of the ligand. In summary, the parameterization analysis of phosphine ligands and pyridine bases in general revealed dominant influences of steric effects that may imply the formation of bis-ligated palladium species and a detrimental role of pyridine coordination in active catalysts. In sequence, we report our efforts to integrate data science and computational chemistry tools to guide, predict, and explain ...