In structure-based drug design we rely on the 3D structure of the biological target to assist in increasing the effectiveness of a drug. The aim is to use this structural information as a guide to design more potent compounds; this is typically done through optimizing the binding affinity of a single bound-state.

This approach is largely reliant on the approximation that drug binding follows a simple two-state model. In reality, drug binding often involves many states each with their own kinetic barriers.

Focusing on a single state of the ligand is not always sufficient, when optimizing for selectivity between closely related receptor subtypes, binding sites can be nearly identical. It then becomes necessary to optimize the drug for regions outside the binding site, poorly represented by experimental structures.

We have used molecular dynamics simulations to investigate the trajectory of a drug en route to its binding site in atomic detail, revealing intermediate “metastable” binding sites where the ligand pauses for a significant amount of time before resuming its journey. Our hypothesis is that if these metastable binding sites can be characterized they can then be used as targets for selectivity in rational drug design.

In order to identify and characterize metastable binding sites we have constructed a Markov state model. This allows us to characterize the kinetics for not only the final state, but also between each other intermediate state on the binding pathway, thereby identifying major and minor pathways that we can compare between similar systems.