Molecular recognition, by design

The Howard Lab builds predictive models of host-guest binding and uses them to design and optimize chemosensors and supramolecular asymmetric catalysts.

Our group combines electronic structure calculations, machine learning on strategically designed binding datasets, and synthetic supramolecular chemistry.

What we do

• Predictive binding models. Machine learning on association constants and computed descriptors of host–guest complexes.
• Chemosensing. Differential sensor arrays for analytes that resist single-receptor detection.
• Asymmetric catalysis. Using recognition-driven models to rationalize and predict enantioselectivity.
• Open tooling. Python packages for extracting and validating properties from quantum chemistry output.

News

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Interested in joining? See Group Members for open positions, or write to us from the Contact page.