Stefan Doerr

2.5k total citations · 1 hit paper
13 papers, 1.6k citations indexed

About

Stefan Doerr is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Stefan Doerr has authored 13 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 8 papers in Materials Chemistry and 6 papers in Computational Theory and Mathematics. Recurrent topics in Stefan Doerr's work include Protein Structure and Dynamics (11 papers), Machine Learning in Materials Science (6 papers) and Computational Drug Discovery Methods (6 papers). Stefan Doerr is often cited by papers focused on Protein Structure and Dynamics (11 papers), Machine Learning in Materials Science (6 papers) and Computational Drug Discovery Methods (6 papers). Stefan Doerr collaborates with scholars based in Spain, United States and Germany. Stefan Doerr's co-authors include Gianni De Fabritiis, Frank Noé, Gerard Martínez-Rosell, José Jiménez-Luna, Alexander Rose, M J Harvey, Nuria Plattner, Toni Giorgino, Maciej Majewski and Raimondas Galvelis and has published in prestigious journals such as Bioinformatics, The Journal of Physical Chemistry B and Scientific Reports.

In The Last Decade

Stefan Doerr

13 papers receiving 1.6k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

DeepSite: protein-binding site predictor using 3D-convolutional neural networks

2017 500 citations José Jiménez-Luna, Stefan Doerr et al. Bioinformatics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Stefan Doerr Spain	11	1.2k	562	503	125	89	13	1.6k
Moritz Hoffmann Switzerland	9	1.0k 0.9×	362 0.6×	291 0.6×	113 0.9×	53 0.6×	16	1.5k
Sergio Decherchi Italy	20	853 0.7×	452 0.8×	217 0.4×	97 0.8×	52 0.6×	71	1.5k
Lauren Raguette United States	5	1.1k 0.9×	243 0.4×	228 0.5×	115 0.9×	78 0.9×	7	1.6k
Sergei Grudinin France	21	1.0k 0.9×	204 0.4×	437 0.9×	117 0.9×	121 1.4×	66	1.3k
Lee‐Wei Yang Taiwan	16	1.7k 1.5×	296 0.5×	664 1.3×	188 1.5×	87 1.0×	42	2.1k
Rafaël Najmanovich Canada	29	1.8k 1.5×	634 1.1×	491 1.0×	115 0.9×	66 0.7×	60	2.3k
Boris Aguilar United States	14	1.3k 1.1×	291 0.5×	284 0.6×	86 0.7×	69 0.8×	34	1.9k
Carlos X. Hernández United States	8	1.6k 1.4×	250 0.4×	498 1.0×	249 2.0×	124 1.4×	12	2.1k
Natalya S. Bogatyreva Russia	17	1.6k 1.4×	437 0.8×	365 0.7×	107 0.9×	60 0.7×	29	2.4k

Countries citing papers authored by Stefan Doerr

Since Specialization

Citations

This map shows the geographic impact of Stefan Doerr's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Stefan Doerr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Doerr more than expected).

Fields of papers citing papers by Stefan Doerr

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Stefan Doerr. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Stefan Doerr. The network helps show where Stefan Doerr may publish in the future.

Co-authorship network of co-authors of Stefan Doerr

This figure shows the co-authorship network connecting the top 25 collaborators of Stefan Doerr. A scholar is included among the top collaborators of Stefan Doerr based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Stefan Doerr. Stefan Doerr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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13 of 13 papers shown

Peláez, Raúl P., Raimondas Galvelis, Peter Eastman, et al.. (2024). TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations. Journal of Chemical Theory and Computation. 20(10). 4076–4087. 31 indexed citations

Torrens‐Fontanals, Mariona, et al.. (2024). PlayMolecule Viewer: A Toolkit for the Visualization of Molecules and Other Data. Journal of Chemical Information and Modeling. 64(3). 584–589. 10 indexed citations

Galvelis, Raimondas, Alejandro Varela‐Rial, Stefan Doerr, et al.. (2023). NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics. Journal of Chemical Information and Modeling. 63(18). 5701–5708. 49 indexed citations

Theodoropoulou, Anastasia, Stefan Doerr, John I. Manchester, et al.. (2022). Membrane Composition and Raf[CRD]-Membrane Attachment Are Driving Forces for K-Ras4B Dimer Stability. The Journal of Physical Chemistry B. 126(7). 1504–1519. 6 indexed citations

Varela‐Rial, Alejandro, et al.. (2022). PlayMolecule Glimpse: Understanding Protein–Ligand Property Predictions with Interpretable Neural Networks. Journal of Chemical Information and Modeling. 62(2). 225–231. 13 indexed citations

Doerr, Stefan, Maciej Majewski, Adrià Pérez, et al.. (2021). TorchMD: A Deep Learning Framework for Molecular Simulations. Journal of Chemical Theory and Computation. 17(4). 2355–2363. 167 indexed citations

Galvelis, Raimondas, Stefan Doerr, João M. Damas, M J Harvey, & Gianni De Fabritiis. (2019). A Scalable Molecular Force Field Parameterization Method Based on Density Functional Theory and Quantum-Level Machine Learning. Journal of Chemical Information and Modeling. 59(8). 3485–3493. 63 indexed citations

Ferruz, Noelia, Stefan Doerr, Michelle Vanase‐Frawley, et al.. (2018). Dopamine D3 receptor antagonist reveals a cryptic pocket in aminergic GPCRs. Scientific Reports. 8(1). 897–897. 44 indexed citations

Plattner, Nuria, Stefan Doerr, Gianni De Fabritiis, & Frank Noé. (2017). Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling. Nature Chemistry. 9(10). 1005–1011. 251 indexed citations

10.

Jiménez-Luna, José, Stefan Doerr, Gerard Martínez-Rosell, Alexander Rose, & Gianni De Fabritiis. (2017). DeepSite: protein-binding site predictor using 3D-convolutional neural networks. Bioinformatics. 33(19). 3036–3042. 500 indexed citations breakdown →

11.

Doerr, Stefan, Toni Giorgino, Gerard Martínez-Rosell, João M. Damas, & Gianni De Fabritiis. (2017). High-Throughput Automated Preparation and Simulation of Membrane Proteins with HTMD. Journal of Chemical Theory and Computation. 13(9). 4003–4011. 23 indexed citations

12.

Doerr, Stefan, M J Harvey, Frank Noé, & Gianni De Fabritiis. (2016). HTMD: High-Throughput Molecular Dynamics for Molecular Discovery. Journal of Chemical Theory and Computation. 12(4). 1845–1852. 297 indexed citations

13.

Doerr, Stefan & Gianni De Fabritiis. (2014). On-the-Fly Learning and Sampling of Ligand Binding by High-Throughput Molecular Simulations. Journal of Chemical Theory and Computation. 10(5). 2064–2069. 134 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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