Quantitatively assessing protein conformational dynamics and ligand dissociation are two problems of critical importance for computer-aided drug discovery. Both of these problems involve larger shifts in the protein conformation than are ordinarily considered in drug discovery efforts. Even though it is well known that proteins are best described as a dynamic ensemble of states, actually acquiring a representative ensemble, especially one with probabilities attached to states, has remained an elusive problem. Molecular dynamics can in theory capture the full ensemble with a long enough simulation but it would take millions of years to simulate the timescale needed to study drug binding or unbinding. Given this timescale problem, it is necessary to develop software solutions to accelerate the sampling of these important rare events. A number of enhanced sampling methods such as metadynamics have arisen to deal with this problem but the methods that are able to attain the fastest speedup also require a low-dimensional description of the system's dynamics. In this thesis, I will develop methods to describe protein dynamics with low-dimensional functions that can be used with enhanced sampling and apply these methods in an enhanced sampling pipeline. The methods developed will both perform variable selection finding a small set of descriptors for the protein dynamics and perform manifold learning to find a low-dimensional representation of the dynamics using this set of descriptions. This pipeline will be used to tackle both problems of conformational dynamics and ligand dissociation in a relatively automated manner. I will then describe how solving these problems in a high throughput manner could impact structure-based drug design efforts, and the work remaining to attain that goal.