Predictive computational methods and experimental studies for the discovery of carbohydrate-based catalysts

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Molecular discovery is often a long, inefficient and expensive process. Our research integrates organic, computational, and medicinal chemistry in order to increase the output rate in both synthetic catalysis and drug discovery. ACE 1.0 (Asymmetric Catalyst Evaluation) and FITTED 2.2 (Flexibility Induced Through Targeted Evolutionary Description) have been developed to identify asymmetric catalysts and enzyme inhibitors respectively. Continuing this trend this proposal presents improvements of the molecular mechanics-based program ACE currently funded by NSERC and its application to the design and synthesis of a variety of carbohydrate-based bifunctional catalysts. ACE, which is the only independent software of its kind, quickly and accurately predicts the stereoselectivity of asymmetric reactions. Although this program is accurate on two modeled reactions, improvements are necessary to make it more versatile and more user-friendly for organic chemists such as implementation/evaluation of implicit solvent models, development of force field transition metal parameters accounting for various geometries of specific metals and modeling of the Boltzmann population. Other optimizations are also proposed and will convert ACE into a virtual screening tool and a de novo optimization tool. We will also modify ACE to use ChemDraw files as input.