Chirality, which is the property of an object to be non-superimposable to its mirror image, is ubiquitous across the natural world. Life is built on chiral molecules, and most biological systems recognise enantiomers, which is one of the two mirror images of a chiral molecule. Achieving only one enantiomer of chiral molecules is of immense interest both to academic research and industrial processes. Pharmaceutical and agrochemical industries have major interest in the chiral market considering its relevance and enormous impact in both human health and drug safety. Asymmetric synthesis contributes to the majority of the chiral technology market and it rests on the rapid development of homogenous transition metal catalysts. In this context, computational chemistry is a powerful technique contributing knowledge that can be used to design better performing catalysts. Within this PhD thesis, computational chemistry has been used to understand the reaction mechanism and the origin of enantioselectivity in prototypical asymmetric reactions. Attention has focused on catalytic systems involving late-transition metals, which have been widely applied in industry. The main goal was to provide a computational support to the development of catalysts leading to higher enantioselection based on mechanistic insights. Three case studies described in this thesis, Ru-catalyzed asymmetric olefin metathesis, Ir-catalyzed asymmetric intramolecular hydroamination, and Rh-catalyzed asymmetric conjugate addition, show how modeling tools can accelerate the understanding of chemical behavior and can aid in the design of novel catalysts within improved selectivity.
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