| 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 | 866 | 2009 |
| 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), 07B604_1, 2013 | 808 | 2013 |
| 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 | 645 | 2011 |
| 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 | 605 | 2018 |
| 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 | 391 | 2017 |
| 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 | 333 | 2016 |
| 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 | 322 | 2019 |
| 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 | 322 | 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 | 293 | 2017 |
| Emergence of Multiple EGFR Extracellular Mutations during Cetuximab Treatment in Colorectal CancerEGFR Mutations and Resistance to Cetuximab S Arena, B Bellosillo, G Siravegna, A Martínez, I Cañadas, L Lazzari, ... Clinical cancer research 21 (9), 2157-2166, 2015 | 258 | 2015 |
| Foundations of dissipative particle dynamics EG Flekkøy, PV Coveney, G De Fabritiis Physical Review E 62 (2), 2140, 2000 | 216 | 2000 |
| 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 | 199 | 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 | 174 | 2014 |
| 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 | 164 | 2019 |
| 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 | 155 | 2017 |
| Multiscale modeling of liquids with molecular specificity G De Fabritiis, R Delgado-Buscalioni, PV Coveney Physical review letters 97 (13), 134501, 2006 | 131 | 2006 |
| Machine learning for protein folding and dynamics F Noé, G De Fabritiis, C Clementi Current opinion in structural biology 60, 77-84, 2020 | 117 | 2020 |
| Induced effects of sodium ions on dopaminergic G-protein coupled receptors J Selent, F Sanz, M Pastor, G De Fabritiis PLoS Computational Biology 6 (8), e1000884, 2010 | 115 | 2010 |
| Kinetic characterization of the critical step in HIV-1 protease maturation SK Sadiq, F Noé, G De Fabritiis Proceedings of the National Academy of Sciences 109 (50), 20449-20454, 2012 | 113 | 2012 |
| Fluctuating hydrodynamic modeling of fluids at the nanoscale G De Fabritiis, M Serrano, R Delgado-Buscalioni, PV Coveney Physical Review E 75 (2), 026307, 2007 | 112 | 2007 |
Toni GiorginoConsiglio Nazionale delle RicercheVerified email at cnr.it
