🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge

Gianni De Fabritiis

Universitat Pompeu Fabra

ORCID: 0000-0003-3913-4877
Biochemistry, Genetics and Molecular BiologyComputer ScienceMaterials Science

Pilot corpus only

This score is computed over theSindex pilot corpus and does not cover the full scientific literature. Scores are relative to papers we have ingested — papers, authors, and institutions outside the pilot are not represented. Methodology.

Top 1%percentile
4.6Author DataRank

Indexed papers

2in pilot corpus
datarank_citation_only_1hop_v6· scope data_onlyMethodology
Why this DataRank?

An author's DataRank is the sum of the DataRanks of all 2 indexed papers attributed to them. A prolific author with many moderate-impact papers can outrank one with a single high-impact paper.

Author scores recompute whenever paper DataRanks are refreshed, so this number lags the underlying paper scores by at most one batch run.

Read the full methodology →

Top data-sharing exemplar

The highest-impact dataset this researcher has shared, ranked by DataRank — the single contribution doing the most to lift their data-sharing standing.

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

Scientific Data(2023)10.1038/s41597-022-01882-6
Top 7%
3.3DataRank
Top 7%171 citations

Pavan Kumar Behara, David Dotson, Raimondas Galvelis, John E. Herr, Joshua T. Horton +7 more

Papers

Driven by 12 papers — median percentile 89. Top paper: “SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials”.

SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

Scientific Data(2023)10.1038/s41597-022-01882-6
Top 7%
3.3DataRank
Top 7%171 citations

Pavan Kumar Behara, David Dotson, Raimondas Galvelis, John E. Herr, Joshua T. Horton +7 more

mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics

Scientific Data(2024)10.1038/s41597-024-04140-z
Top 16%
1.3DataRank
Top 16%41 citations

Toni Giorgino, Gianni De Fabritiis, Antonio Mirarchi

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv (Cornell University)(2023)10.48550/arxiv.2310.03121
N/A
0.457DataRank · unranked
20 citations

Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio +20 more

NNP/MM: Accelerating Molecular Dynamics Simulations with Machine Learning Potentials and Molecular Mechanics

Journal of Chemical Information and Modeling(2023)10.1021/acs.jcim.3c00773
N/A
0DataRank · unranked
92 citations

Alejandro Varela‐Rial, Stefan Doerr, Roberto Fino, Peter Eastman, Thomas E. Markland +3 more

Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series

Journal of Chemical Information and Modeling(2023)10.1021/acs.jcim.3c00178
N/A
0DataRank · unranked
15 citations

Adrià Pérez, Maciej Majewski, Emilio Gallicchio, Gianni De Fabritiis, Francesc Sabanés Zariquiey

TensorNet: Cartesian Tensor Representations for Efficient Learning of Molecular Potentials

arXiv (Cornell University)(2023)10.48550/arxiv.2306.06482
N/A
0DataRank · unranked
18 citations

Gianni De Fabritiis, Guillem Simeon

Enhancing Protein–Ligand Binding Affinity Predictions Using Neural Network Potentials

Journal of Chemical Information and Modeling(2024)10.1021/acs.jcim.3c02031
N/A
0DataRank · unranked
52 citations

Raimondas Galvelis, Emilio Gallicchio, John D. Chodera, Thomas E. Markland, Gianni De Fabritiis +1 more

TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations

Journal of Chemical Theory and Computation(2024)10.1021/acs.jctc.4c00253
N/A
0DataRank · unranked
52 citations

Guillem Simeon, Raimondas Galvelis, Antonio Mirarchi, Peter Eastman, Stefan Doerr +4 more

The Role of Hydrophobic Nodes in the Dynamics of Class A β-Lactamases

Frontiers in Microbiology(2021)10.3389/fmicb.2021.720991
N/A
0DataRank · unranked
16 citations

Junqi Yin, Adrià Pérez, Gianni De Fabritiis, Robert A. Bonomo, Debsindhu Bhowmik +2 more

Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes

arXiv (Cornell University)(2024)10.48550/arxiv.2403.15073
N/A
0DataRank · unranked
0 citations

Antonio Mirarchi, Raúl P. Peláez, Raimondas Galvelis, Gianni De Fabritiis, Guillem Simeon

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

The Journal of Physical Chemistry B(2023)10.1021/acs.jpcb.3c06662
N/A
0DataRank · unranked
495 citations

Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio +20 more

Machine learning coarse-grained potentials of protein thermodynamics

Nature Communications(2023)10.1038/s41467-023-41343-1
N/A
0DataRank · unranked
117 citations

Adrià Pérez, Philipp Thölke, Stefan H. Doerr, Nicholas E. Charron, Toni Giorgino +5 more