  Significant progress in computer hardware and software have enabled molecular
dynamics (MD) simulations to model complex biological phenomena such as protein
folding. However, enabling MD simulations to access biologically relevant
timescales (e.g., beyond milliseconds) still remains challenging. These
limitations include (1) quantifying which set of states have already been
(sufficiently) sampled in an ensemble of MD runs, and (2) identifying novel
states from which simulations can be initiated to sample rare events (e.g.,
sampling folding events). With the recent success of deep learning and
artificial intelligence techniques in analyzing large datasets, we posit that
these techniques can also be used to adaptively guide MD simulations to model
such complex biological phenomena. Leveraging our recently developed
unsupervised deep learning technique to cluster protein folding trajectories
into partially folded intermediates, we build an iterative workflow that
enables our generative model to be coupled with all-atom MD simulations to fold
small protein systems on emerging high performance computing platforms. We
demonstrate our approach in folding Fs-peptide and the $\beta\beta\alpha$ (BBA)
fold, FSD-EY. Our adaptive workflow enables us to achieve an overall root-mean
squared deviation (RMSD) to the native state of 1.6$~\AA$ and 4.4~$\AA$
respectively for Fs-peptide and FSD-EY. We also highlight some emerging
challenges in the context of designing scalable workflows when data intensive
deep learning techniques are coupled to compute intensive MD simulations.
