| 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 | 644 | 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 | 412 | 2016 |
| 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 | 357 | 2017 |
| 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 | 197 | 2021 |
| 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 | 196 | 2014 |
| A scalable molecular force field parameterization method based on density functional theory and quantum-level machine learning R Galvelis, S Doerr, JM Damas, MJ Harvey, G De Fabritiis Journal of chemical information and modeling 59 (8), 3485-3493, 2019 | 89 | 2019 |
| Machine learning coarse-grained potentials of protein thermodynamics M Majewski, A Pérez, P Thölke, S Doerr, NE Charron, T Giorgino, ... Nature communications 14 (1), 5739, 2023 | 65 | 2023 |
| Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs N Ferruz, S Doerr, MA Vanase-Frawley, Y Zou, X Chen, ES Marr, ... Scientific reports 8 (1), 897, 2018 | 54 | 2018 |
| NNP/MM: accelerating molecular dynamics simulations with machine learning potentials and molecular mechanics R Galvelis, A Varela-Rial, S Doerr, R Fino, P Eastman, TE Markland, ... Journal of chemical information and modeling 63 (18), 5701-5708, 2023 | 36 | 2023 |
| AdaptiveBandit: a multi-armed bandit framework for adaptive sampling in molecular simulations A Pérez, P Herrera-Nieto, S Doerr, G De Fabritiis Journal of chemical theory and computation 16 (7), 4685-4693, 2020 | 36 | 2020 |
| Dimensionality reduction methods for molecular simulations S Doerr, I Ariz-Extreme, MJ Harvey, G De Fabritiis arXiv preprint arXiv:1710.10629, 2017 | 31 | 2017 |
| High-throughput automated preparation and simulation of membrane proteins with HTMD S Doerr, T Giorgino, G Martínez-Rosell, JM Damas, G De Fabritiis Journal of Chemical Theory and Computation 13 (9), 4003-4011, 2017 | 27 | 2017 |
| Kinetic characterization of fragment binding in AmpC β-lactamase by high-throughput molecular simulations P Bisignano, S Doerr, MJ Harvey, AD Favia, A Cavalli, G De Fabritiis Journal of Chemical Information and Modeling 54 (2), 362-366, 2014 | 26 | 2014 |
| TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations RP Pelaez, G Simeon, R Galvelis, A Mirarchi, P Eastman, S Doerr, ... Journal of Chemical Theory and Computation, 2024 | 18 | 2024 |
| Protein-protein association and binding mechanism resolved in atomic detail N Plattner, S Doerr, GD Fabritiis, F Noé Nat. Chem 9 (1005-1011), 1, 2017 | 16 | 2017 |
| PlayMolecule glimpse: understanding protein–ligand property predictions with interpretable neural networks A Varela-Rial, I Maryanow, M Majewski, S Doerr, N Schapin, ... Journal of chemical information and modeling 62 (2), 225-231, 2022 | 14 | 2022 |
| NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics R Galvelis, A Varela-Rial, S Doerr, R Fino, P Eastman, TE Markland, ... arXiv preprint arXiv:2201.08110, 2022 | 7 | 2022 |
| Membrane composition and Raf [CRD]-Membrane attachment are driving forces for K-Ras4B dimer stability I Andreadelis, S Kiriakidi, C Lamprakis, A Theodoropoulou, S Doerr, ... The Journal of Physical Chemistry B 126 (7), 1504-1519, 2022 | 5 | 2022 |
| PlayMolecule Viewer: a toolkit for the visualization of molecules and other data M Torrens-Fontanals, P Tourlas, S Doerr, G De Fabritiis Journal of Chemical Information and Modeling 64 (3), 584-589, 2024 | 4 | 2024 |
| Membrane Composition and Raf [CRD]-Membrane Attachment Are Driving Forces for K-Ras4B Dimer Stability Z Cournia, I Andreadelis, S Kiriakidi, C Lamprakis, A Theodoropoulou, ... | 1 | 2021 |