Our projects are focused on the development of synthetic methodology based on organometallic chemistry and asymmetric catalysis, and applied to the synthesis and functionalization of heterocyclic systems, potentially active compounds, natural products or drugs. We also carry out interdisciplinary projects that involve computational chemistry and machine learning (ML) tools for reactivity prediction, and for the design of biologically active molecules.

Our research interests are summarized in the following topics:

Pd(0) and Pd(II): Heck reactions and C-H activation

We are working in developing methodologies based on Pd(0) and Pd(II) catalysis .We have shown that Pd(0)-catalyzed Heck-type or direct C-H arylation reactions and Pd(II)-catalyzed C-H alkenylation or C-H acylation reactions are versatile and effective tools for the synthesis of polyfunctionalized medium-size rings and of drug-like compounds. Asymmetric variants, including central and axial chirality control, and cascade reactions have also been developed. Some examples are shown:

Co(III)-catalyzed C-H activation reactions

We have recently begun to study the application of more abundant and less toxic metals, such as cobalt. We have developed hydroarylation, allylation, and aminocarbonylation reactions of arenes and heteroarenes, using stable Cp*Co(III) complexes as precatalysts. Some examples: 

Machine learning (ML) and artificial intelligence (AI) are becoming increasingly useful techniques for more rationally selecting reaction conditions and the structure of chiral catalysts or ligands. Our group has introduced Perturbation Theory-Machine Learning (PTML) methodology for developing novel multi-target computational (chemo-informatics) models capable of predicting the reactivity or enantioselectivity of a given reaction when structural modifications or changes in reaction conditions are incorporated.

PTML chemo-informatics models are also useful for performing computational (in silico) screening of large compound libraries for the discovery of potential biological activities in drug discovery processes. These models can also be applied to complex systems involving nanoparticles, an emerging area in nanoscience. While the main difficulties of classical chemoinformatics models are the inability to simultaneously predict multiple biological activity parameters of drugs against different target proteins, cell lines, test organisms, etc., our models allow the prediction of multiple biological assay parameters (e.g., IC50, ED50, Kᵢ, Kₘ, etc.) and cross-reading assay conditions (different target proteins, cell lines, organisms), as well as multiple components of delivery systems (nanoparticles, polymers, coatings, linkers, etc.)

Some examples: