| Identification of slow molecular order parameters for Markov model construction G Pérez-Hernández, F Paul, T Giorgino, G De Fabritiis, F Noé The Journal of chemical physics 139 (1), 2013 | 964 | 2013 |
| ACEMD: Accelerating biomolecular dynamics in the microsecond time scale MJ Harvey, G Giupponi, G De Fabritiis Journal of Chemical Theory and Computation 5 (6), 1632-1639, 2009 | 952 | 2009 |
| KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks J Jiménez, M Skalic, G Martinez-Rosell, G De Fabritiis Journal of chemical information and modeling 58 (2), 287-296, 2018 | 798 | 2018 |
| Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations I Buch, T Giorgino, G De Fabritiis Proceedings of the National Academy of Sciences 108 (25), 10184, 2011 | 701 | 2011 |
| DeepSite: protein-binding site predictor using 3D-convolutional neural networks J Jiménez, S Doerr, G Martínez-Rosell, AS Rose, G De Fabritiis Bioinformatics 33 (19), 3036-3042, 2017 | 538 | 2017 |
| Machine learning of coarse-grained molecular dynamics force fields J Wang, S Olsson, C Wehmeyer, A Pérez, NE Charron, G De Fabritiis, ... ACS central science 5 (5), 755-767, 2019 | 435 | 2019 |
| HTMD: high-throughput molecular dynamics for molecular discovery S Doerr, MJ Harvey, F Noé, G De Fabritiis Journal of chemical theory and computation 12 (4), 1845-1852, 2016 | 382 | 2016 |
| An implementation of the smooth particle mesh Ewald method on GPU hardware MJ Harvey, G De Fabritiis Journal of Chemical Theory and Computation 5 (9), 2371-2377, 2009 | 373 | 2009 |
| Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling N Plattner, S Doerr, G De Fabritiis, F Noé Nature chemistry 9 (10), 1005-1011, 2017 | 340 | 2017 |
| Emergence of Multiple EGFR Extracellular Mutations during Cetuximab Treatment in Colorectal Cancer S Arena, B Bellosillo, G Siravegna, A Martínez, I Canadas, L Lazzari, ... Clinical cancer research 21 (9), 2157-2166, 2015 | 281 | 2015 |
| PlayMolecule ProteinPrepare: a web application for protein preparation for molecular dynamics simulations G Martínez-Rosell, T Giorgino, G De Fabritiis Journal of chemical information and modeling 57 (7), 1511-1516, 2017 | 226 | 2017 |
| Foundations of dissipative particle dynamics EG Flekkøy, PV Coveney, G De Fabritiis Physical Review E 62 (2), 2140, 2000 | 223 | 2000 |
| Shape-based generative modeling for de novo drug design M Skalic, J Jiménez, D Sabbadin, G De Fabritiis Journal of chemical information and modeling 59 (3), 1205-1214, 2019 | 217 | 2019 |
| High-throughput all-atom molecular dynamics simulations using distributed computing I Buch, MJ Harvey, T Giorgino, DP Anderson, G De Fabritiis Journal of chemical information and modeling 50 (3), 397-403, 2010 | 210 | 2010 |
| On-the-fly learning and sampling of ligand binding by high-throughput molecular simulations S Doerr, G De Fabritiis Journal of chemical theory and computation 10 (5), 2064-2069, 2014 | 187 | 2014 |
| TorchMD: A deep learning framework for molecular simulations S Doerr, M Majewski, A Pérez, A Kramer, C Clementi, F Noe, T Giorgino, ... Journal of chemical theory and computation 17 (4), 2355-2363, 2021 | 161 | 2021 |
| Machine learning for protein folding and dynamics F Noé, G De Fabritiis, C Clementi Current opinion in structural biology 60, 77-84, 2020 | 160 | 2020 |
| TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials P Thölke, G De Fabritiis International Conference on Learning Representations, 2021, 2022 | 153 | 2022 |
| Coarse graining molecular dynamics with graph neural networks BE Husic, NE Charron, D Lemm, J Wang, A Pérez, M Majewski, A Krämer, ... The Journal of chemical physics 153 (19), 2020 | 152 | 2020 |
| Multiscale modeling of liquids with molecular specificity G De Fabritiis, R Delgado-Buscalioni, PV Coveney Physical review letters 97 (13), 134501, 2006 | 134 | 2006 |
MJ HarveyImperial College London在 imperial.ac.uk 的电子邮件经过验证
