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There has been remarkable progress in asymmetric catalysis since the inception of the field three decades ago and, chiefly, over the last decade. Because of this, asymmetric catalysis now provides chemical researchers in both academia and industry with the means to directly access useful enantiomerically enriched compounds. With advances in technology (i.e. high throughput screening), the identification of an asymmetric catalyst that promotes a transformation in high enantiomeric excess has been expedited. However, the approach to catalyst identification remains mainly empirical, wherein evaluation of a significant number of ligands, often structurally unrelated, is required to develop a mature chiral catalyst. Therefore, the central goal of our program is focused on developing general methods that facilitate the rapid design and optimization of new asymmetric catalysts for challenging, synthetically useful transformations. The lecture will focus on our recent efforts to evaluate structure-enantioselectivity relationships as a function of ligand structure to facilitate catalyst design and optimization. A particular focus will be on classic physical organic mechanistic tools in combination with multi-dimensional statistical approaches.

Olfaction researchers at all levels are ultimately trying to solve the same problem, namely a transform across three spaces: from the physicochemical space of odor molecules, through the brain space of neural activity, and on to the space of odor perception and its ensuing behavioral decisions. To solve these transforms, one has to be able to measure each one of these spaces independently. As each of these three spaces is apparently of very high dimensionality, we applied principal components analysis (PCA) to data in each of these three domains. We observed that the functional dimensionality of these spaces was significantly lower than their apparent dimensionality. Moreover, the key axis (PC1) was correlated across domains. In other words, the key axis of olfactory perception was correlated with the key axis of odorant structure, and both of these were correlated with the key axis of neural activity in the olfactory system across species. These correlations allowed us to construct a modest but significant predictive framework across domains. In other words, we could now look at the structure of a novel molecule, and predict modest but significant aspects of its perception and ensuing neural activity across species. Beyond this predictive framework, our approach has several implications regarding sensory phenomena within a metric space. For example, it implies a point of sensory convergence where all olfactory mixtures should smell the same. We call this point "olfactory white". Our metric approach also implies points (odors) that are at the upper and lower boundaries of this metric space, and should therefore be odorless. We call these points "infra smell" and "ultra smell". In this talk I will consider the implications of this approach, as well as its potential shortcomings, and their possible solutions.