Computational Explorations of Unconventional Approaches to Control Noncovalent Interactions

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PROJECT SUMMARY The research interests of my group are rooted in explorations of new and useful conceptual models to improve the control and prediction of noncovalent interactions. Our research involves the use of a variety of computational quantum chemical tools, applications of density functional theory (DFT), cheminformatics, and machine-learning methods. A premise of our research is that aromaticity may be used to modulate many types of noncovalent interactions (such as hydrogen bonding, π-stacking, anion-π interactions). The reciprocal relationship we find, between “aromaticity” in molecules and the strengths of “noncovalent interactions,” is surprising especially since they are typically considered as largely separate ideas in chemistry. The innovation of this research is that it will enable use of intuitive “back-of-the-envelope” electron-counting rules (such as the 4n+2πe Hückel rule for aromaticity) to make predictions of experimental outcomes regarding the impact of noncovalent interactions. A five-year goal is to realize the use of our conceptual models in real synthetic examples prepared by our experimental collaborators. My research vision is to bridge discoveries of innovative concepts to their practical impacts for biomedical and biomolecular research.