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Progress and challenges in the automated construction of Markov state models for full protein systems, , , and . The Journal of Chemical Physics, (2009)Using path sampling to build better Markovian state models: Predicting the folding rate and mechanism of a tryptophan zipper beta hairpin, , and . Journal of Chemical Physics, 121 (1): 415-425 (2004)Everything you wanted to know about Markov State Models but were afraid to ask, , and . Methods, 52 (1): 99--105 (2010)Retrosynthetic reaction prediction using neural sequence-to-sequence models., , , , , , , , , and . CoRR, (2017)Decision functions from supervised machine learning algorithms as collective variables for accelerating molecular simulations., and . CoRR, (2018)Hard Data on Soft Errors: A Large-Scale Assessment of Real-World Error Rates in GPGPU, and . CoRR, (2009)Note: Variational Encoding of Protein Dynamics Benefits from Maximizing Latent Autocorrelation., and . CoRR, (2018)Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation., , , , and . CoRR, (2018)Folding and misfolding of the collagen triple helix: Markov analysis of molecular dynamics simulations, , and . BIOPHYSICAL JOURNAL, 93 (12): 4108-4115 (December 2007)Meeting halfway on the bridge between protein folding theory and experiment. Proceedings of National Academy of Sciences, 100 (7): 3555--3556 (April 2003)