Gerard Martínez-Rosell
- Molecular Biology top 10%
- Computational Theory and Mathematics top 0.5%
- Materials Chemistry top 10%
- Pharmacology top 10%
- Organic Chemistry
- Co-authors
- Gianni De FabritiisJosé Jiménez-LunaMiha ŠkaličStefan DoerrAlexander RoseToni GiorginoM J HarveyAdrià Pérez
- Topics
- Protein Structure and Dynamics (10 papers)Computational Drug Discovery Methods (9 papers)Machine Learning in Materials Science (5 papers)
- Partner nations
- SpainUnited StatesItaly
In The Last Decade
Gerard Martínez-Rosell
13 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 117
- Molecular Biology 1.3k
- Computational Theory and Mathematics 934
- Materials Chemistry 444
- Pharmacology 142
- Organic Chemistry 118
Countries citing papers authored by Gerard Martínez-Rosell
This map shows the geographic impact of Gerard Martínez-Rosell'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 Gerard Martínez-Rosell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gerard Martínez-Rosell more than expected).
Fields of papers citing papers by Gerard Martínez-Rosell
This network shows the impact of papers produced by Gerard Martínez-Rosell. 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 Gerard Martínez-Rosell. The network helps show where Gerard Martínez-Rosell may publish in the future.
Co-authorship network of co-authors of Gerard Martínez-Rosell
This figure shows the co-authorship network connecting the top 25 collaborators of Gerard Martínez-Rosell. A scholar is included among the top collaborators of Gerard Martínez-Rosell 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 Gerard Martínez-Rosell. Gerard Martínez-Rosell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 23 | |
| 2 | 48 | |
| 3 | 29 | |
| 4 | 25 | |
| 5 | 46 | |
| 6 | 18 | |
| 7 | KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networksbreakdown → | 637 |
| 8 | 42 | |
| 9 | 39 | |
| 10 | 225 | |
| 11 | DeepSite: protein-binding site predictor using 3D-convolutional neural networksbreakdown → | 500 |
| 12 | 23 | |
| 13 | 29 |
About Gerard Martínez-Rosell
Gerard Martínez-Rosell is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry, having authored 13 papers that have together received 1.7k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (10 papers), Computational Drug Discovery Methods (9 papers) and Machine Learning in Materials Science (5 papers). The work is most often cited by research in Computational Theory and Mathematics (934 citations), Molecular Biology (1.3k citations) and Materials Chemistry (444 citations). Gerard Martínez-Rosell has collaborated with scholars based in Spain, United States and Italy. Frequent co-authors include Gianni De Fabritiis, José Jiménez-Luna, Miha Škalič, Stefan Doerr, Alexander Rose, Toni Giorgino, M J Harvey, Adrià Pérez, Alejandro Varela‐Rial and Marta Filizola. Their work appears in journals such as Bioinformatics, Scientific Reports and Chemical Science.
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.