Noncovalent interactions are ubiquitous in organic systems, and can play decisive roles in the outcome of asymmetric organocatalytic reactions. Their prevalence, combined with the often subtle line separating favorable dispersion interactions from unfavorable steric interactions, often complicates the identification of the particular noncovalent interactions responsible for stereoselectivity. Ultimately, the stereoselectivity of most organocatalytic reactions hinges on the balance of both favorable and unfavorable noncovalent interactions in the stereocontrolling transition state (TS). In this Account, we provide an overview of our attempts to understand the role of noncovalent interactions in organocatalyzed reactions and to develop new computational tools for organocatalyst design. Following a brief discussion of noncovalent interactions involving aromatic rings and the associated challenges capturing these effects computationally, we summarize two examples of chiral phosphoric acid catalyzed reactions in which noncovalent interactions play pivotal, although somewhat unexpected, roles. In the first, List's catalytic asymmetric Fischer indole reaction, we show that both π-stacking and CH/π interactions of the substrate with the 3,3'-aryl groups of the catalyst impact the stability of the stereocontrolling TS. However, these noncovalent interactions oppose each other, with π-stacking interactions stabilizing the TS leading to one enantiomer and CH/π interactions preferentially stabilizing the competing TS. Ultimately, the CH/π interactions dominate and, when combined with hydrogen bonding interactions, lead to preferential formation of the observed product. In the second example, a series of phosphoric acid catalyzed asymmetric ring openings of meso-epoxides, we show that noncovalent interactions of the substrates with the 3,3'-aryl groups of the catalyst play only an indirect role in stereoselectivity. Instead, the stereoselectivity of these reactions are driven by the electrostatic stabilization of a fleeting partial positive charge in the SN2-like transition state by the chiral electrostatic environment of the phosphoric acid catalyst. Next, we describe our studies of bipyridine N-oxide and N,N'-dioxide catalyzed alkylation reactions. Based on several examples, we demonstrate that there are many potential arrangements of ligands around a hexacoordinate silicon in the stereocontrolling TS, and one must consider all of these in order to identify the lowest-lying TS structures. We also present a model in which electrostatic interactions between a formyl CH group and a chlorine in these TSs underlie the enantioselectivity of these reactions. Finally, we discuss our efforts to develop computational tools for the screening of potential organocatalyst designs, starting in the context of bipyridine N,N'-dioxide catalyzed alkylation reactions. Our new computational tool kit (AARON) has been used to design highly effective catalysts for the asymmetric propargylation of benzaldehyde, and is currently being used to screen...

Despite the ubiquity of stacking interactions between heterocycles and aromatic amino acids in biological systems, our ability to predict their strength, even qualitatively, is limited. On the basis of rigorous ab initio data, we developed simple predictive models of the strength of stacking interactions between heterocycles commonly found in biologically active molecules and the amino acid side chains Phe, Tyr, and Trp. These models provide reliable predictions of the stacking ability of a given heterocycle based on readily computed heterocycle descriptors, eliminating the need for quantum chemical computations of stacked dimers. We show that the values of these descriptors, and therefore the strength of stacking interactions with aromatic amino acid side chains, follow predictable trends and can be modulated by changing the number and distribution of heteroatoms within the heterocycle. This provides a simple conceptual means for understanding stacking interactions in protein binding sites and tuning their strength in the context of drug design.

The development of asymmetric catalysts is typically driven by the experimental screening of potential catalyst designs. Herein, we demonstrate the design of asymmetric propargylation catalysts through computational screening. This was done using our computational toolkit AARON (automated alkylation reaction optimizer for N-oxides), which automates the prediction of enantioselectivities for bidentate Lewis base catalyzed alkylation reactions. A systematic screening of 59 potential catalysts built on 6 bipyridine N,N′-dioxide-derived scaffolds results in predicted ee values for the propargylation of benzaldehyde ranging from 45% (S) to 99% (R), with 12 ee values exceeding 95%. These data provide a broad set of experimentally testable predictions. Moreover, the associated data revealed key details regarding the role of stabilizing electrostatic interactions in asymmetric propargylations, which were harnessed in the design of a propargylation catalyst for which the predicted ee exceeds 99%.

Complexes of 9-methyladenine with 46 heterocycles commonly found in drugs were located using dispersion-corrected density functional theory, providing a representative set of 408 unique stacked dimers. The predicted binding enthalpies for each heterocycle span a broad range, highlighting the strong dependence of heterocycle stacking interactions on the relative orientation of the interacting rings. Overall, the presence of NH and carbonyl groups lead to the strongest stacking interactions with 9-methyadenine, and the strength of π-stacking interactions is sensitive to the distribution of heteroatoms within the ring as well as the specific tautomer considered. Although molecular dipole moments provide a sound predictor of the strengths and orientations of the 28 monocyclic heterocycles considered, dipole moments for the larger fused heterocycles show very little correlation with the predicted binding enthalpies.

<p>Despite the ubiquity of stacking interactions between heterocycles and aromatic amino acids in biological systems, our ability to predict their strength, even qualitatively, is limited. Based on rigorous <i>ab initio</i> data, we have devised a simple predictive model of the strength of stacking interactions between heterocycles commonly found in biologically active molecules and the amino acid side chains Phe, Tyr, and Trp. This model provides rapid predictions of the stacking ability of a given heterocycle based on readily-computed heterocycle descriptors. We show that the values of these descriptors, and therefore the strength of stacking interactions with aromatic amino acid side chains, follow simple predictable trends and can be modulated by changing the number and distribution of heteroatoms within the heterocycle. This provides a simple conceptual model for understanding stacking interactions in protein binding sites and optimizing inhibitor binding in drug design.</p>