  Molecular dynamics (MD) has become a powerful tool for studying biophysical
systems, due to increasing computational power and availability of software.
Although MD has made many contributions to better understanding these complex
biophysical systems, there remain methodological difficulties to be surmounted.
First, how to make the deluge of data generated in running even a microsecond
long MD simulation human comprehensible. Second, how to efficiently sample the
underlying free energy surface and kinetics. In this short perspective, we
summarize machine learning based ideas that are solving both of these
limitations, with a focus on their key theoretical underpinnings and remaining
challenges.
