Investigation of enantiodiscrimination using computational approaches

Abstract

Enantiomers are chiral molecules that are specular mirror images of each other but cannot be superimposed. These compounds have gained attention for their applications in various fields, including pharmaceuticals, agriculture, and food. The importance of enantiomers is due to their distinct interactions with other chiral molecules, which can result in different biological or pharmaceutical activities. Separating enantiomeric mixtures is a crucial task for designing the applications of each component of the enantiomeric pair and is a challenge for modern science. New drug molecules with a chiral center to be approved by the FDA must contain only one enantiomer. The Matrix-Assisted DOSY approach, which uses a Chiral Solving Agent (CSA) as a matrix, is a technique applied in the current Ph.D. project (FAPESP 2021/05081-5) that shows the possibility of differentiating enantiomers using Nuclear Magnetic Resonance. However, there is limited experimental evidence to explain, at the molecular level, the interaction between diastereoisomeric complexes that reflects the observed experimental discriminations in the frequency and diffusion domains. Recent studies have demonstrated that computational approaches using Density Functional Theory and Molecular Dynamics can help to understand systems at the microscopic level and provide insights that are difficult or impossible to obtain experimentally. Therefore, computational simulations of CSA-enantiomer complexes are proposed in this project to evaluate the binding sites, interaction energies, conformational equilibria, the temporal evolution of the diastereoisomeric interaction, the diffusion process, solvent effects, and complexation stoichiometry, to help explain the frequency and diffusion discrimination of four enantiomers in which BINOL was employed as CSA. (AU)