[11] Lukas D Schuler, Xavier Daura, and Wilfred F Van Gunsteren. An improved gromos96 force field for aliphatic hydrocarbons in the condensed phase. Journal of computational chemistry, 22(11):1205-1218, 2001.
[12] Markus Christen, Philippe H Hünenberger, Dirk Bakowies, Riccardo Baron, Roland Bürgi, Daan P Geerke, Tim N Heinz, Mika A Kastenholz, Vincent Krautler, Chris Oostenbrink, et al. The gromos software for biomolecular simulation: Gromos05. Journal of computational chemistry, 26(16):1719-1751, 2005.
[13] Stefan Chmiela, Alexandre Tkatchenko, Huziel E Sauceda, Igor Poltavsky, Kristof T Schütt, and Klaus-Robert Müller. Machine learning of accurate energy-conserving molecular force fields. Science advances, 3(5):e1603015, 2017.
[14] Stefan Chmiela, Huziel E Sauceda, Klaus-Robert Müller, and Alexandre Tkatchenko. Towards exact molecular dynamics simulations with machine-learned force fields. Nature communications, 9(1):3887, 2018.
[15] Huziel E Sauceda, Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller, and Alexandre Tkatchenko. Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces. The Journal of chemical physics, 150(11):114102, 2019.
[16] Stefan Chmiela, Huziel E Sauceda, Igor Poltavsky, Klaus-Robert Müller, and Alexandre Tkatchenko. sgdml: Constructing accurate and data efficient molecular force fields using machine learning. Computer Physics Communications, 240:38-45, 2019.
[17] Jiang Wang, Simon Olsson, Christoph Wehmeyer, Adrià Pérez, Nicholas E Charron, Gianni De Fabritiis, Frank Noé, and Cecilia Clementi. Machine learning of coarse-grained molecular dynamics force fields. ACS central science, 5(5):755-767, 2019.
[18] Feliks Nuske, Lorenzo Boninsegna, and Cecilia Clementi. Coarse-graining molecular systems by spectral matching. The Journal of chemical physics, 151(4):044116, 2019.
[19] Jiang Wang, Stefan Chmiela, Klaus-Robert Müller, Frank Noé, and Cecilia Clementi. Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach. The Journal of chemical physics, 152(19):194106, 2020.
[20] Xiang Fu, Tian Xie, Nathan J Rebello, Bradley D Olsen, and Tommi Jaakkola. Simulate time-integrated coarse-grained molecular dynamics with geometric machine learning. arXiv preprint arXiv:2204.10348, 2022.
[21] David E Shaw, Peter J Adams, Asaph Azaria, Joseph A Bank, Brannon Batson, Alistair Bell, Michael Bergdorf, Jhanvi Bhatt, J Adam Butts, Timothy Correia, et al. Anton 3: twenty microseconds of molecular dynamics simulation before lunch. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pages 1-11, 2021.
[22] Ye Li, Xianren Zhang, and Dapeng Cao. The role of shape complementarity in the protein-protein interactions. Scientific reports, 3(1):3271, 2013.
[23] Pablo Gainza, Freyr Sverrisson, Frederico Monti, Emanuele Rodola, D Boscaini, MM Bronstein, and BE Correia. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nature Methods, 17(2):184-192, 2020.
[24] Pablo Gainza, Sarah Wehrle, Alexandra Van Hall-Beauvais, Anthony Marchand, Andreas Scheck, Zander Harteveld, Stephen Buckley, Dongchun Ni, Shuguang Tan, Freyr Sverrisson, et al. De novo design of site-specific protein interactions with learned surface fingerprints. bioRxiv, pages 2022–06, 2022.
[25] Freyr Sverrisson, Jean Feydy, Bruno E Correia, and Michael M Bronstein. Fast end-to-end learning on protein surfaces. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15272-15281, 2021.