Marco Giulini

810 total citations · 1 hit paper
16 papers, 290 citations indexed

About

Marco Giulini is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Marco Giulini has authored 16 papers receiving a total of 290 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 8 papers in Materials Chemistry and 5 papers in Computational Theory and Mathematics. Recurrent topics in Marco Giulini's work include Protein Structure and Dynamics (10 papers), Machine Learning in Materials Science (4 papers) and Computational Drug Discovery Methods (4 papers). Marco Giulini is often cited by papers focused on Protein Structure and Dynamics (10 papers), Machine Learning in Materials Science (4 papers) and Computational Drug Discovery Methods (4 papers). Marco Giulini collaborates with scholars based in Italy, Netherlands and Austria. Marco Giulini's co-authors include Raffaello Potestio, Roberto Menichetti, Alexandre M. J. J. Bonvin, M. Scott Shell, Zuzana Jandová, Jorge Roel‐Touris, Victor Reys, Rodrigo V. Honorato, Panagiotis I. Koukos and Brian Jiménez‐García and has published in prestigious journals such as Bioinformatics, Nature Protocols and Science Advances.

In The Last Decade

Marco Giulini

15 papers receiving 287 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.

The HADDOCK2.4 web server for integrative modeling of biomolecular complexes

2024 142 citations Rodrigo V. Honorato, Mikaël Trellet et al. Nature Protocols profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Marco Giulini Italy	8	188	89	28	25	23	16	290
Mark A. Hallen United States	13	333 1.8×	40 0.4×	51 1.8×	8 0.3×	24 1.0×	22	404
Xinqiang Ding United States	13	363 1.9×	70 0.8×	66 2.4×	12 0.5×	8 0.3×	19	444
Yi Xiao China	12	520 2.8×	135 1.5×	18 0.6×	10 0.4×	8 0.3×	48	639
Güngör Özer United States	7	280 1.5×	43 0.5×	16 0.6×	6 0.2×	14 0.6×	8	353
Ali Sinan Saglam United States	6	172 0.9×	36 0.4×	35 1.3×	5 0.2×	9 0.4×	7	212
Yannick G. Spill France	10	405 2.2×	54 0.6×	39 1.4×	6 0.2×	10 0.4×	13	489
Duy Phuoc Tran Japan	13	430 2.3×	83 0.9×	82 2.9×	14 0.6×	52 2.3×	22	562
Pooja Suresh United States	7	232 1.2×	47 0.5×	41 1.5×	3 0.1×	33 1.4×	9	296
Lichun He China	9	236 1.3×	68 0.8×	23 0.8×	3 0.1×	16 0.7×	29	370
Spyridon Vicatos United States	8	393 2.1×	110 1.2×	20 0.7×	6 0.2×	19 0.8×	9	475

Countries citing papers authored by Marco Giulini

Since Specialization

Citations

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

Fields of papers citing papers by Marco Giulini

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marco Giulini. 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 Marco Giulini. The network helps show where Marco Giulini may publish in the future.

Co-authorship network of co-authors of Marco Giulini

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

All Works

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16 of 16 papers shown

Toft‐Bertelsen, Trine L., Andreas Prestel, Cagla Sahin, et al.. (2025). The SH protein of mumps virus is a druggable pentameric viroporin. Science Advances. 11(23). eads3071–eads3071. 1 indexed citations

Giulini, Marco, Victor Reys, João M. C. Teixeira, et al.. (2025). HADDOCK3: A Modular and Versatile Platform for Integrative Modeling of Biomolecular Complexes. Journal of Chemical Information and Modeling. 65(13). 7315–7324. 3 indexed citations

Giulini, Marco, et al.. (2024). Improved prediction of antibody and their complexes with clustered generative modelling ensembles. Bioinformatics Advances. 5(1). vbaf161–vbaf161.

Honorato, Rodrigo V., Mikaël Trellet, Brian Jiménez‐García, et al.. (2024). The HADDOCK2.4 web server for integrative modeling of biomolecular complexes. Nature Protocols. 19(11). 3219–3241. 142 indexed citations breakdown →

Reys, Victor, Marco Giulini, Vlad Cojocaru, et al.. (2024). Integrative Modeling in the Age of Machine Learning: A Summary of HADDOCK Strategies in CAPRI Rounds 47–55. Proteins Structure Function and Bioinformatics. 1 indexed citations

Giulini, Marco, et al.. (2024). ARCTIC-3D: automatic retrieval and clustering of interfaces in complexes from 3D structural information. Communications Biology. 7(1). 49–49. 2 indexed citations

Giulini, Marco, Constantin Schneider, Daniel Cutting, et al.. (2024). Towards the accurate modelling of antibody−antigen complexes from sequence using machine learning and information-driven docking. Bioinformatics. 40(10). 12 indexed citations

Giulini, Marco, et al.. (2024). Modeling Protein–Glycan Interactions with HADDOCK. Journal of Chemical Information and Modeling. 64(19). 7816–7825. 4 indexed citations

Giulini, Marco, et al.. (2024). EXCOGITO, an Extensible Coarse-Graining Toolbox for the Investigation of Biomolecules by Means of Low-Resolution Representations. Journal of Chemical Information and Modeling. 64(12). 4912–4927. 3 indexed citations

10.

Giulini, Marco, et al.. (2022). Making sense of complex systems through resolution, relevance, and mapping entropy. Physical review. E. 106(4). 44101–44101. 9 indexed citations

11.

Menichetti, Roberto, Marco Giulini, & Raffaello Potestio. (2021). A journey through mapping space: characterising the statistical and metric properties of reduced representations of macromolecules. The European Physical Journal B. 94(10). 204–204. 12 indexed citations

12.

Giulini, Marco, et al.. (2021). From System Modeling to System Analysis: The Impact of Resolution Level and Resolution Distribution in the Computer-Aided Investigation of Biomolecules. Frontiers in Molecular Biosciences. 8. 676976–676976. 31 indexed citations

13.

Errica, Federico, Marco Giulini, Davide Bacciu, et al.. (2021). A Deep Graph Network–Enhanced Sampling Approach to Efficiently Explore the Space of Reduced Representations of Proteins. Frontiers in Molecular Biosciences. 8. 637396–637396. 12 indexed citations

14.

Giulini, Marco, Roberto Menichetti, M. Scott Shell, & Raffaello Potestio. (2020). An Information-Theory-Based Approach for Optimal Model Reduction of Biomolecules. Institutional Research Information System (Università degli Studi di Trento). 46 indexed citations

15.

Giulini, Marco & Raffaello Potestio. (2019). A deep learning approach to the structural analysis of proteins. Interface Focus. 9(3). 20190003–20190003. 5 indexed citations

16.

Chierici, Marco, et al.. (2018). Machine learning models for predicting endocrine disruption potential of environmental chemicals. Journal of Environmental Science and Health Part C. 36(4). 237–251. 7 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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