Alejandro Varela‐Rial

525 citations
8 papers · 198 · h-index 6

Impact in

Papers in

Alejandro Varela‐Rial

8 papers receiving 196 citations

Peers

Alejandro Varela‐Rial
Comparison fields: 5 of 64
  • Computational Theory and Mathematics 110
  • Molecular Biology 113
  • Materials Chemistry 79
  • Biophysics 7
  • Health Informatics 1
Replace Hanxuan Cai with:
Hanxuan Cai China
Linbu Liao China
Jintu Zhang China
Lieyang Chen United States
Edvard Lindelöf Sweden
Lina Humbeck Germany
Ilia Igashov Switzerland
Manon Réau France
Talia B. Kimber Germany
Ada Sedova United States
Alejandro Varela‐Rial relative to Hanxuan Cai China Hanxuan Cai's profile →
Citations per field
00.5×1.5×
Hanxuan Cai · 1×
Citations per year

Countries citing papers authored by Alejandro Varela‐Rial

Since Specialization
Citations

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

Fields of papers citing papers by Alejandro Varela‐Rial

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Alejandro Varela‐Rial. 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 Alejandro Varela‐Rial. The network helps show where Alejandro Varela‐Rial may publish in the future.

Co-authors

The 24 scholars most cited alongside Alejandro Varela‐Rial, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Alejandro Varela‐Rial Line = papers co-authored together Alejandro Varela‐Rial links everyone, so they are left out of the graph.

All Works

8 of 8 papers shown
#Work
1 202354
2 201846
3 202142
4 202320
5 202215
6 201712
7 20245
8 20244

About Alejandro Varela‐Rial

Alejandro Varela‐Rial is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Ecology and Oncology, having authored 8 papers that have together received 198 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Machine Learning in Materials Science (3 papers), Protein Structure and Dynamics (3 papers), Polyomavirus and related diseases (2 papers), Bacteriophages and microbial interactions (2 papers), RNA Research and Splicing (2 papers), Enzyme Structure and Function (1 paper) and Receptor Mechanisms and Signaling (1 paper). The work is most often cited by research in Computational Theory and Mathematics (110 citations), Molecular Biology (113 citations), Materials Chemistry (79 citations), Biophysics (7 citations) and Health Informatics (1 citation). Alejandro Varela‐Rial has collaborated with scholars based in Spain, Germany and Bulgaria. Frequent co-authors include Gianni De Fabritiis, Maciej Majewski, José Jiménez-Luna, Gérard Martinez, Miha Škalič, Stefan Doerr, Raimondas Galvelis, Peter Eastman, Thomas E. Markland and John D. Chodera. Their work appears in journals such as eLife, Journal of Chemical Information and Modeling, Wiley Interdisciplinary Reviews Computational Molecular Science, Biotechnology and Applied Biochemistry and Bioinformatics.

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.

Explore authors with similar magnitude of impact