Adrià Pérez

1.2k total citations · 1 hit paper
11 papers, 778 citations indexed

About

Adrià Pérez is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Adrià Pérez has authored 11 papers receiving a total of 778 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Molecular Biology, 8 papers in Materials Chemistry and 5 papers in Computational Theory and Mathematics. Recurrent topics in Adrià Pérez's work include Protein Structure and Dynamics (9 papers), Machine Learning in Materials Science (5 papers) and Computational Drug Discovery Methods (5 papers). Adrià Pérez is often cited by papers focused on Protein Structure and Dynamics (9 papers), Machine Learning in Materials Science (5 papers) and Computational Drug Discovery Methods (5 papers). Adrià Pérez collaborates with scholars based in Spain, United States and Germany. Adrià Pérez's co-authors include Gianni De Fabritiis, Frank Noé, Cecilia Clementi, Nicholas E. Charron, Jiang Wang, Simon Olsson, Christoph Wehmeyer, Maciej Majewski, Andreas Krämer and Toni Giorgino and has published in prestigious journals such as Nature Communications, Scientific Reports and Construction and Building Materials.

In The Last Decade

Adrià Pérez

11 papers receiving 768 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.

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

2019 343 citations Jiang Wang, Simon Olsson et al. ACS Central Science profile →

Peers

Adrià Pérez

MC

MB

CTM

AMPO

BE

Aldo Glielmo Italy

Hythem Sidky United States

Luigi Bonati Italy

Julija Zavadlav Slovenia

Abhinav Jain United States

Valerio Rizzi Switzerland

Michele Invernizzi Switzerland

Ying Wai Li United States

James Chapman United States

Eric J. Rawdon United States

Aldo Glielmo Italy

Adrià Pérez

444 ×0.9

MC

208 ×0.5

MB

213 ×1.1

CTM

97 ×1.2

AMPO

71 ×1.1

BE

Citations per year, relative to Adrià Pérez Adrià Pérez (= 1×) peers Aldo Glielmo

Countries citing papers authored by Adrià Pérez

Since Specialization

Citations

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

Fields of papers citing papers by Adrià Pérez

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Adrià Pérez

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

All Works

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11 of 11 papers shown

1.

Pérez, Adrià, et al.. (2024). ‘Onion-peel’ cracking and spalling in coupled meso-mechanical analysis of External Sulfate Attack in concrete using zero-thickness interface elements. Construction and Building Materials. 455. 139011–139011. 4 indexed citations

2.

Majewski, Maciej, Adrià Pérez, Stefan H. Doerr, et al.. (2023). Machine learning coarse-grained potentials of protein thermodynamics. Nature Communications. 14(1). 5739–5739. 62 indexed citations

3.

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

4.

Pérez, Adrià, et al.. (2023). Validation of the Alchemical Transfer Method for the Estimation of Relative Binding Affinities of Molecular Series. Journal of Chemical Information and Modeling. 63(8). 2438–2444. 11 indexed citations

5.

Yin, Junqi, Adrià Pérez, Gianni De Fabritiis, et al.. (2021). The Role of Hydrophobic Nodes in the Dynamics of Class A β-Lactamases. Frontiers in Microbiology. 12. 720991–720991. 8 indexed citations

6.

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

7.

Pérez, Adrià, et al.. (2020). Characterization of partially ordered states in the intrinsically disordered N-terminal domain of p53 using millisecond molecular dynamics simulations. Scientific Reports. 10(1). 12402–12402. 18 indexed citations

8.

Pérez, Adrià, et al.. (2020). Small Molecule Modulation of Intrinsically Disordered Proteins Using Molecular Dynamics Simulations. Journal of Chemical Information and Modeling. 60(10). 5003–5010. 11 indexed citations

9.

Husic, Brooke E., Nicholas E. Charron, Dominik Lemm, et al.. (2020). Coarse graining molecular dynamics with graph neural networks. Refubium (Universitätsbibliothek der Freien Universität Berlin). 121 indexed citations

10.

Wang, Jiang, Simon Olsson, Christoph Wehmeyer, et al.. (2019). Machine Learning of Coarse-Grained Molecular Dynamics Force Fields. ACS Central Science. 5(5). 755–767. 343 indexed citations breakdown →

11.

Pérez, Adrià, Gerard Martínez-Rosell, & Gianni De Fabritiis. (2018). Simulations meet machine learning in structural biology. Current Opinion in Structural Biology. 49. 139–144. 29 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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