.. _bibliography:

Bibliography
============

Dataset & Simulation References
-------------------------------

.. [AdaptiveStrategy] Doerr, S. and De Fabritiis, G. `On-the-fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations <https://pubs.acs.org/doi/10.1021/ct400919u>`_ . Journal of Chemical Theory and Computation, 10(5). 2064-2069. (2014).

.. [ACEMD] Harvey, M.J. et al. `ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale <https://pubs.acs.org/doi/10.1021/ct9000685>`_ . Journal of Chemical Theory and Computation, 5(6). 1632-1639. (2009).

.. [TIP3P] Jorgensen, W.L. et al. `Comparison of simple potential functions for simulating water <https://aip.scitation.org/doi/10.1063/1.445869>`_ . J. Chem. Phys. 926(79). (1983).

.. [CHARM22Star] Piana, S., et al. `How Robust Are Protein Folding Simulations with Respect to Force Field Parameterization? <https://doi.org/10.1016/j.bpj.2011.03.051>`_ Biophys J. 100. 47-49. (2001).

.. [AMBER_ff_99SB_ILDN] Hornak, V. et al. `Comparison of multiple Amber force fields and development of improved protein backbone parameters <https://onlinelibrary.wiley.com/doi/10.1002/prot.21123>`_ . Proteins: Structure, Function, and Bioinformatics, 65(3), 712–725. (2006).


CGnet References
----------------

.. [CGnet] Wang, J. et al. `Machine Learning of Coarse-Grained Molecular Dynamics Force Fields <https://doi.org/10.1021/acscentsci.8b00913>`_ . ACS Cent. Sci. 5, 755–767 (2019).

.. [CGSchnet] Husic, B. E. et al. `Coarse graining molecular dynamics with graph neural networks <https://doi.org/10.1063/5.0026133>`_ . J. Chem. Phys. 153, 194101 (2020).

.. [TransCGSchnet] Charron, N. E. et al `Navigating protein landscapes with a machine-learned transferable coarse-grained model <https://arxiv.org/abs/2310.18278>`_ - arXiv:2310.18278 (2023)



Neural Network Architectures & Implementations
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.. [Schnet] Schütt, K. T. et al. `SchNet – A deep learning architecture for molecules and materials <https://doi.org/10.1063/1.5019779>`_ J. Chem. Phys. 148(24), 241722. (2018).

.. [PT_geom_schnet] https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/nn/models/schnet.html

.. [Physnet] Unke, O. T. et al. `PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges. <https://doi.org/10.1021/acs.jctc.9b00181>`_ Journal of Chemical Theory and Computation, 15(6). 3678–3693. (2019).

.. [MACE] Batatia, I. et al. `MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields <https://proceedings.neurips.cc/paper_files/paper/2022/hash/4a36c3c51af11ed9f34615b81edb5bbc-Abstract-Conference.html>`_  Advances in Neural Information Processing Systems, 35. 11423-11436 (2022)