Stefan Doerr
Title
Cited by
Cited by
Year
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
1372016
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, 2017
1182017
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
1102014
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
852017
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
212014
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), 1-10, 2018
142018
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
142017
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
122017
Dimensionality reduction methods for molecular simulations
S Doerr, I Ariz-Extreme, MJ Harvey, G De Fabritiis
arXiv preprint arXiv:1710.10629, 2017
112017
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
22019
PlayMolecule Parameterize: a Scalable Molecular Force Field Parameterization Method Based on Quantum-Level Machine Learning
R Galvelis, S Doerr, JM Damas, MJ Harvey, G De Fabritiis
arXiv preprint arXiv:1907.06952, 2019
12019
AdaptiveBandit: A multi-armed bandit framework for adaptive sampling in molecular simulations
A Pérez, P Herrera-Nieto, S Doerr, G De Fabritiis
arXiv preprint arXiv:2002.12582, 2020
2020
Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs (vol 8, 897, 2018)
N Ferruz, S Doerr, MA Vanase-Frawley, Y Zou, X Chen, ES Marr, ...
SCIENTIFIC REPORTS 9, 2019
2019
Author Correction: 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 9 (1), 1-4, 2019
2019
Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs
N Ferruz Capapey, S Doerr, MA Vanase Frawley, Y Zou, X Chen, ES Marr, ...
Sci Rep 2018 Dec; 8 (1): 897, 2018
2018
Applications of machine learning in molecular simulations: Transcending barriers
S Doerr
Universitat Pompeu Fabra, 2018
2018
Optimizing Proteins and Ligands for Computerized Drug Discovery
J Damas, A Cuzzolin, R Galvelis, S Doerr, G Martínez-Rosell, M Harvey, ...
MDPI AG, 2017
2017
DeepSite: Protein binding site predictor using 3D-convolutional neural networks. Supplementary Data.
J Jiménez, S Doerr, G Martınez, AS Rose, G De Fabritiis
2017
HTMD: A complete software workspace for simulation-guided drug design
S Doerr, M Harvey, G De Fabritiis
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 250, 2015
2015
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Articles 1–19