Mohammad M. Sultan, Hannah K. Wayment-Steele, and Vijay S. Pande. Transferable Neural Networks for Enhanced Sampling of Protein Dynamics. Journal of Chemical Theory and Computation, 14(4):1887-1894, April 2018.
Lixin Sun, Jonathan Vandermause, Simon Batzner, Yu Xie, David Clark, Wei Chen, and Boris Kozinsky. Multitask machine learning of collective variables for enhanced sampling of rare events. Journal of Chemical Theory and Computation, 18(4):2341-2353, 2022.
Erwin Schrödinger. Über die umkehrung der natürgesetze. Verlag der Akademie der Wissenschaften in Kommission bei Walter De Gruyter u . . ., 1931.
Erwin Schrödinger. Sur la théorie relativiste de l'électron et l'interprétable de la mécanique quantique. In Annales de l'institut Henri Poincaré, volume 2, pages 269-310, 1932.
Francisco Vargas, Pierre Thodoroff, Austen Lamacraft, and Neil Lawrence. Solving schrödinger bridges via maximum likelihood. Entropy, 23(9):1134, 2021.
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet. Diffusion Schrödinger bridge with applications to score-based generative modeling. Advances in Neural Information Processing Systems, 34, 2021.
Yongxin Chen, Tryphon T Georgiou, and Michele Pavon. On the relation between optimal transport and Schrödinger bridges: A stochastic control viewpoint. Journal of Optimization Theory and Applications, 169(2):671-691, 2016.
Hilbert J Kappen. An introduction to stochastic control theory, path integrals and reinforcement learning. In AIP conference proceedings, volume 887, pages 149-181. American Institute of Physics, 2007.
Sep Thijsen and H. J. Kappen. Path integral control and state-dependent feedback. Phys. Rev. E, 91: 032104, Mar 2015. doi: 10.1103/PhysRevE.91.032104. URL https://link.aps.org/doi/10.1103/PhysRevE.91.032104.
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang. Learning gradient fields for molecular conformation generation. In International Conference on Machine Learning, pages 9558-9568. PMLR, 2021.
Taco Cohen and Max Welling. Group equivariant convolutional networks. In International conference on machine learning, pages 2990-2999. PMLR, 2016.
Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling. E(n) equivariant graph neural networks. arXiv preprint arXiv:2102.09844, 2021.
Peter Eastman, Jason Swails, John D Chodera, Robert T McGibbon, Yutong Zhao, Kyle A Beauchamp, Lee-Ping Wang, Andrew C Simmonett, Matthew P Harrigan, Chaya D Stern, et al. OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS computational biology, 13(7):e1005659, 2017.
Shinya Honda, Kazuhiko Yamasaki, Yoshito Sawada, and Hisayuki Morii. 10 residue folded peptide designed by segment statistics. Structure, 12(8):1507-1518, 2004.
M Marvin Seibert, Alexandra Patriksson, Berk Hess, and David Van Der Spoel. Reproducible polypeptide folding and structure prediction using molecular dynamics simulations. Journal of molecular biology, 354(1):173-183, 2005.
Daisuke Satoh, Kentaro Shimizu, Shugo Nakamura, and Tohru Terada. Folding free-energy landscape of a 10-residue mini-protein, chignolin. FEBS letters, 580(14):3422-3426, 2006.
Cristina Paissoni and Carlo Camilloni. How to determine accurate conformational ensembles by metadynamics metainference: a chignolin study case. Frontiers in molecular biosciences, 8:491, 2021.
Ryuhei Harada and Akio Kitao. Exploring the folding free energy landscape of a  $\beta$ -hairpin miniprotein, chignolin, using multiscale free energy landscape calculation method. The Journal of Physical Chemistry B, 115(27):8806-8812, 2011.