Gianni De Fabritiis

12.4k total citations · 7 hit papers
119 papers, 7.9k citations indexed

About

Gianni De Fabritiis is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Gianni De Fabritiis has authored 119 papers receiving a total of 7.9k indexed citations (citations by other indexed papers that have themselves been cited), including 86 papers in Molecular Biology, 53 papers in Materials Chemistry and 38 papers in Computational Theory and Mathematics. Recurrent topics in Gianni De Fabritiis's work include Protein Structure and Dynamics (60 papers), Computational Drug Discovery Methods (37 papers) and Machine Learning in Materials Science (31 papers). Gianni De Fabritiis is often cited by papers focused on Protein Structure and Dynamics (60 papers), Computational Drug Discovery Methods (37 papers) and Machine Learning in Materials Science (31 papers). Gianni De Fabritiis collaborates with scholars based in Spain, United Kingdom and United States. Gianni De Fabritiis's co-authors include M J Harvey, Toni Giorgino, Gerard Martínez-Rosell, Stefan Doerr, Frank Noé, José Jiménez-Luna, G. Giupponi, Miha Škalič, Ignasi Buch and Peter V. Coveney and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Physical Review Letters.

In The Last Decade

Gianni De Fabritiis

118 papers receiving 7.8k citations

Hit Papers

ACEMD: Accelerating Biomolecular Dynamics in the Microsec... 2009 2026 2014 2020 2009 2018 2013 2011 2017 200 400 600
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.

  1. ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale
    2009 704 citations Gianni De Fabritiis et al. Journal of Chemical Theory and Computation profile →
  2. KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networks
    2018 637 citations José Jiménez-Luna, Gerard Martínez-Rosell et al. Journal of Chemical Information and Modeling profile →
  3. Identification of slow molecular order parameters for Markov model construction
    2013 529 citations Guillermo Pérez‐Hernández, Fabian Paul et al. profile →
  4. Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations
    2011 518 citations Ignasi Buch, Toni Giorgino et al. Proceedings of the National Academy of Sciences profile →
  5. DeepSite: protein-binding site predictor using 3D-convolutional neural networks
    2017 500 citations José Jiménez-Luna, Stefan Doerr et al. profile →
  6. Machine Learning of Coarse-Grained Molecular Dynamics Force Fields
    2019 343 citations Adrià Pérez, Gianni De Fabritiis et al. profile →
  7. SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials
    2023 104 citations Peter Eastman, Raimondas Galvelis et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gianni De Fabritiis Spain 40 5.4k 2.5k 2.3k 683 562 119 7.9k
Sándor Vajda United States 58 12.0k 2.2× 2.8k 1.2× 3.5k 1.5× 606 0.9× 334 0.6× 239 16.6k
John L. Klepeis United States 24 7.0k 1.3× 1.9k 0.8× 1.8k 0.8× 819 1.2× 769 1.4× 33 10.6k
Haim J. Wolfson Israel 61 11.7k 2.2× 3.7k 1.5× 3.3k 1.4× 704 1.0× 273 0.5× 176 16.5k
Nathan Baker United States 42 10.6k 2.0× 2.2k 0.9× 1.3k 0.6× 628 0.9× 1.6k 2.8× 114 15.4k
Nikolay V. Dokholyan United States 66 10.3k 1.9× 2.4k 1.0× 1.1k 0.5× 636 0.9× 426 0.8× 370 14.7k
Attila Gürsoy Türkiye 41 6.4k 1.2× 1.3k 0.5× 1.5k 0.7× 386 0.6× 444 0.8× 134 8.3k
Michael P. Eastwood United States 27 6.2k 1.2× 1.8k 0.7× 1.4k 0.6× 888 1.3× 957 1.7× 42 8.8k
Michael Holst United States 30 5.6k 1.0× 1.0k 0.4× 818 0.4× 293 0.4× 848 1.5× 90 9.2k
Peter Eastman United States 22 4.8k 0.9× 1.5k 0.6× 951 0.4× 663 1.0× 1.2k 2.1× 29 7.2k
Huafeng Xu United States 27 4.9k 0.9× 992 0.4× 1.5k 0.6× 724 1.1× 783 1.4× 63 7.5k

Countries citing papers authored by Gianni De Fabritiis

Since Specialization
Citations

This map shows the geographic impact of Gianni De Fabritiis'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 Gianni De Fabritiis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gianni De Fabritiis more than expected).

Fields of papers citing papers by Gianni De Fabritiis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gianni De Fabritiis. 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 Gianni De Fabritiis. The network helps show where Gianni De Fabritiis may publish in the future.

Co-authorship network of co-authors of Gianni De Fabritiis

This figure shows the co-authorship network connecting the top 25 collaborators of Gianni De Fabritiis. A scholar is included among the top collaborators of Gianni De Fabritiis 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 Gianni De Fabritiis. Gianni De Fabritiis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Peláez, Raúl P., et al.. (2025). Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes. Journal of Chemical Theory and Computation. 21(4). 1831–1837.
2.
Thomas, Morgan, et al.. (2025). REINFORCE-ING Chemical Language Models for Drug Discovery. Journal of Chemical Information and Modeling. 65(23). 12752–12763. 1 indexed citations
3.
Janowski, Robert, Hyun-Seo Kang, Thomas Monecke, et al.. (2024). PURA syndrome-causing mutations impair PUR-domain integrity and affect P-body association. eLife. 13. 5 indexed citations
4.
Peláez, Raúl P., et al.. (2024). AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics. Journal of Chemical Theory and Computation. 20(22). 9871–9878. 4 indexed citations
5.
Galvelis, Raimondas, et al.. (2024). Enhancing Protein–Ligand Binding Affinity Predictions Using Neural Network Potentials. Journal of Chemical Information and Modeling. 64(5). 1481–1485. 32 indexed citations
6.
Janowski, Robert, Hyun-Seo Kang, Thomas Monecke, et al.. (2024). PURA syndrome-causing mutations impair PUR-domain integrity and affect P-body association. eLife. 13. 4 indexed citations
7.
Thomas, Morgan, Mazen Ahmad, Gary Tresadern, & Gianni De Fabritiis. (2024). PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models. Journal of Cheminformatics. 16(1). 77–77. 2 indexed citations
8.
Pérez, Adrià, et al.. (2023). Binding-and-Folding Recognition of an Intrinsically Disordered Protein Using Online Learning Molecular Dynamics. Journal of Chemical Theory and Computation. 19(13). 3817–3824. 4 indexed citations
9.
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
10.
Majewski, Maciej, et al.. (2023). Top-Down Machine Learning of Coarse-Grained Protein Force Fields. Journal of Chemical Theory and Computation. 19(21). 7518–7526. 14 indexed citations
11.
Dainese, Enrico, Sergio Oddi, Annalaura Sabatucci, et al.. (2020). Author Correction: The endocannabinoid hydrolase FAAH is an allosteric enzyme. Scientific Reports. 10(1). 5903–5903. 2 indexed citations
12.
Dainese, Enrico, Sergio Oddi, Annalaura Sabatucci, et al.. (2020). The endocannabinoid hydrolase FAAH is an allosteric enzyme. Scientific Reports. 10(1). 2292–2292. 30 indexed citations
13.
Jiménez-Luna, José, Laura Pérez‐Benito, Gerard Martínez-Rosell, et al.. (2019). DeltaDelta neural networks for lead optimization of small molecule potency. Chemical Science. 10(47). 10911–10918. 48 indexed citations
14.
Pérez‐Hernández, Guillermo, et al.. (2013). Identification of slow molecular order parameters for Markov model construction. 529 indexed citations breakdown →
15.
Buch, Ignasi, Toni Giorgino, & Gianni De Fabritiis. (2011). Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proceedings of the National Academy of Sciences. 108(25). 10184–10189. 518 indexed citations breakdown →
16.
Sadiq, S. Kashif & Gianni De Fabritiis. (2010). Explicit solvent dynamics and energetics of HIV‐1 protease flap opening and closing. Proteins Structure Function and Bioinformatics. 78(14). 2873–2885. 59 indexed citations
17.
Fabritiis, Gianni De, et al.. (2008). Insights from the energetics binding at the domain-ligand of the Src SH2 domain of water interface. BIROn (Birkbeck, University of London). 1 indexed citations
18.
Fabritiis, Gianni De, et al.. (2008). Insights from the energetics of water binding at the domain‐ligand interface of the Src SH2 domain. Proteins Structure Function and Bioinformatics. 72(4). 1290–1297. 9 indexed citations
19.
Fabritiis, Gianni De, Fabio Pammolli, & Massimo Riccaboni. (2003). On size and growth of business firms. 41 indexed citations
20.
Fabritiis, Gianni De. (2002). Multiscale dissipative particle dynamics. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences. 360(1792). 317–331. 10 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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