José Jiménez-Luna
- Computational Theory and Mathematics top 0.2%
- Computational Drug Discovery Methods 17
- Health Informatics top 5%
- Molecular Biology top 5%
- Protein Structure and Dynamics 10
- Metabolomics and Mass Spectrometry Studies 2
- Materials Chemistry top 10%
- Machine Learning in Materials Science 13
- Biophysics top 5%
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- Cosmology and Gravitation Theories 3
- Advanced Differential Geometry Research 2
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- Black Holes and Theoretical Physics 3
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- Microbial Natural Products and Biosynthesis 2
- Co-authors
- Gianni De FabritiisGerard Martínez-RosellMiha ŠkaličGisbert SchneiderStefan DoerrAlexander RoseNils WeskampFrancesca Grisoni
- Journals
- Nature Communications (1 paper)Bioinformatics (3 papers)Physical Chemistry Chemical Physics (1 paper)
- Partner nations
- SpainSwitzerlandGermany
In The Last Decade
José Jiménez-Luna
22 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 139
- Computational Theory and Mathematics 1.4k
- Health Informatics 28
- Molecular Biology 1.3k
- Materials Chemistry 715
- Biophysics 74
Countries citing papers authored by José Jiménez-Luna
This map shows the geographic impact of José Jiménez-Luna'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 José Jiménez-Luna with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites José Jiménez-Luna more than expected).
Fields of papers citing papers by José Jiménez-Luna
This network shows the impact of papers produced by José Jiménez-Luna. 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 José Jiménez-Luna. The network helps show where José Jiménez-Luna may publish in the future.
Co-authorship network
The 25 scholars most cited alongside José Jiménez-Luna, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 13 | |
| 2 | 2023 | 8 | |
| 3 | 2023 | 41 | |
| 4 | 2022 | 92 | |
| 5 | 2022 | 13 | |
| 6 | 2022 | 42 | |
| 7 | 2022 | 18 | |
| 8 | 2021 | 57 | |
| 9 | Artificial intelligence in drug discovery: recent advances and future perspectivesbreakdown → | 2021 | 243 |
| 10 | 2019 | 48 | |
| 11 | 2019 | 150 | |
| 12 | 2018 | 25 | |
| 13 | 2018 | 46 | |
| 14 | KDEEP: Protein–Ligand Absolute Binding Affinity Prediction via 3D-Convolutional Neural Networksbreakdown → | 2018 | 637 |
| 15 | 2018 | 42 | |
| 16 | 2017 | 27 | |
| 17 | DeepSite: protein-binding site predictor using 3D-convolutional neural networksbreakdown → | 2017 | 500 |
| 18 | 1987 | 5 | |
| 19 | 1983 | 23 | |
| 20 | 1979 | 11 |
About José Jiménez-Luna
José Jiménez-Luna is a scholar working on Computational Theory and Mathematics, Biophysics and Materials Chemistry, having authored 22 papers that have together received 2.1k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (13 papers), Protein Structure and Dynamics (10 papers), Cosmology and Gravitation Theories (3 papers), Black Holes and Theoretical Physics (3 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Microbial Natural Products and Biosynthesis (2 papers) and Advanced Differential Geometry Research (2 papers). The work is most often cited by research in Computational Theory and Mathematics (1.4k citations), Health Informatics (28 citations) and Molecular Biology (1.3k citations). José Jiménez-Luna has collaborated with scholars based in Spain, Switzerland and Germany. Frequent co-authors include Gianni De Fabritiis, Gerard Martínez-Rosell, Miha Škalič, Gisbert Schneider, Stefan Doerr, Alexander Rose, Nils Weskamp, Francesca Grisoni, Davide Sabbadin and Kenneth Atz. Their work appears in journals such as Nature Communications, Bioinformatics and Physical Chemistry Chemical Physics.
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.