Atomistic modeling of enantioselection: Applications in chiral chromatography

Summary In this chapter we examined how atomistic molecular modeling is used to address questions concerning enantiodiscrimination in chiral chromatography. For Type I CSPs it is revealed that a variety of strategies are commonly used for sampling microstates accessible to the transient, diastereomeric complexes. One extreme is to rely primarily on chemical intuition and/or knowledge obtained from experiment. These strategies are referred to as “motif-based” search strategies, and they can be effective when used judiciously. Moreover they have the benefit of reducing CPU time that can become problematic for large and flexible CSPs. The other extreme is to let the computer do all the sampling without user intervention, and, a variety of stochastic and deterministic searching techniques have been successfully employed. Examples of all these strategies were presented in this chapter for the sake of comparison. In contrast to Type I stationary phases where molecular modelers explicitly treat the intermolecular interactions between selector and selectand, one finds more use of regression models for Type II-V CSPs. The reason for this is that the shape of these CSPs is, with the exception of cyclodextrin and several synthetic hosts, not well defined or not known at all. Thus all one can do is rely on regression models to divulge information concerning the mechanism of retention and enantioselection for a series of related analytes. These models, albeit lacking a detailed atom-by-atom account of the interactions taking place as analytes percolate through a chromatographic column, nonetheless provide important information concerning where and how chiral recognition takes place. Moreover, these models are capable of making predictions. That is, once the model has been constructed and validated, one can use those same kinds of molecular descriptors to predict what the separation will be for an as yet unknown analyte. The computational tools needed for simulating analyte separation under a variety of chromatographic conditions with various stationary phases, chiral and achiral, gas or liquid, currently exist. However we point out that while these computational tools are powerful when used properly, it is still advantageous to use one's own experience when selecting a CSP for a chiral separation. In this regard, then, we point out the enormous research effort by Roussel [87] and Koppenhoefer [88] who created and maintain CHIRBASE, a graphical molecular database on the separation of enantiomers by gas, liquid and supercritical fluid chromatographies. A more recent and potentially very useful database is CHIRULE, a column selection system, designed by Stauffer and Dessy [89]. Databases like these together with the computational methodologies described above allow one to make a better selection of the chromatographic tools needed for a resolution and provide insights concerning the mechanism of chiral discrimination. Finally, most of the published computational studies directed toward chiral chromatography have been carried out by chromatographers rather than by computational chemists. Most of these scientists look at computational chemistry as an adjunct to their experimental work, but understand the information content derived from molecular simulations can provide valuable information not otherwise available. In that sense they are right. However, most chromatographers are not well versed in computational chemistry and make too many serious errors for their results to be of benefit. So, on the one hand there is a need for computational chemistry but on the other hand too many pitfalls exist for the non-expert to step into. The conclusion one draws from this is that chromatographers should work collaboratively with computational chemists to help them solve their problems. In this regard, then, the future of molecular modeling in the separation sciences looks bright.