Bibliography
Dataset & Simulation References
Doerr, S. and De Fabritiis, G. On-the-fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations . Journal of Chemical Theory and Computation, 10(5). 2064-2069. (2014).
Harvey, M.J. et al. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale . Journal of Chemical Theory and Computation, 5(6). 1632-1639. (2009).
Jorgensen, W.L. et al. Comparison of simple potential functions for simulating water . J. Chem. Phys. 926(79). (1983).
Piana, S., et al. How Robust Are Protein Folding Simulations with Respect to Force Field Parameterization? Biophys J. 100. 47-49. (2001).
Hornak, V. et al. Comparison of multiple Amber force fields and development of improved protein backbone parameters . Proteins: Structure, Function, and Bioinformatics, 65(3), 712–725. (2006).
CGnet References
Wang, J. et al. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields . ACS Cent. Sci. 5, 755–767 (2019).
Husic, B. E. et al. Coarse graining molecular dynamics with graph neural networks . J. Chem. Phys. 153, 194101 (2020).
Charron, N. E. et al Navigating protein landscapes with a machine-learned transferable coarse-grained model - arXiv:2310.18278 (2023)
Neural Network Architectures & Implementations
Schütt, K. T. et al. SchNet – A deep learning architecture for molecules and materials J. Chem. Phys. 148(24), 241722. (2018).
https://pytorch-geometric.readthedocs.io/en/latest/_modules/torch_geometric/nn/models/schnet.html
Unke, O. T. et al. PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments and Partial Charges. Journal of Chemical Theory and Computation, 15(6). 3678–3693. (2019).
Batatia, I. et al. MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields Advances in Neural Information Processing Systems, 35. 11423-11436 (2022)