The central goal of our research is to elucidate the fundamental mechanisms of molecular recognition and binding kinetics through the integration of theoretical frameworks, artificial intelligence, and classical molecular mechanics models. We develop and apply computational methods and deep learning approaches to tackle chemically and medically significant problems. These tools are essential for understanding (bio)molecular function and for designing new molecules with high affinity for their targets. Our systems of interest include current and prospective drug targets, enzyme complexes, and chemical host–guest systems. We actively collaborate with experimentalists and theoreticians, both on campus and beyond.
2025 group party

Kinetics of Ligand-Nanostructure Association

We characterize the kinetics of diffusional enzyme-substrate association. Our software, GeomBD, is designed to assess association rate constants for protein-substrate systems ranging in size from small, single protein systems to large bioengineered multi-enzyme nanostructures.

Drug Binding Thermodynamics and Kinetics

The phosphorylated cyclin-dependent kinase 2 (CDK2) regulates cell cycle through the G1 to S phase transition and its deregulation has been implicated in numerous types of cancer. In addition to thermodynamic contributions, kinetic effects are now increasingly considered as important factor in designing efficient inhibitors for targeting CDK2.

Dynamics and Networks for Enzyme Function

We apply classical and accelerated molecular dynamics, coarse-grained Brownian dynamics, and energy and entropy calculations to study protein communication and function regulation, protonation states and water effects in enzyme function. We collaborate with experimental groups, for examples, our collaborators apply NMR to study protein dynamics, ligand protonation states and mechanisms of chemical catalysis in Tryptophan Synthase.