Gisbert Schneider
- Computational Theory and Mathematics top 0.01%
- Computational Drug Discovery Methods 238
- Microbiology top 0.05%
- Molecular Biology top 0.2%
- Chemical Synthesis and Analysis 99
- Protein Structure and Dynamics 46
- Machine Learning in Bioinformatics 42
- Receptor Mechanisms and Signaling 38
- RNA and protein synthesis mechanisms 34
- Pharmacology top 0.1%
- Microbial Natural Products and Biosynthesis 51
- Organic Chemistry top 0.5%
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- Machine Learning in Materials Science 49
- Co-authors
- Petra SchneiderJan A. HissUli FechnerDaniel RekerTiago RodriguesChristopher D. FjellRobert E. W. HancockFrancesca Grisoni
- Journals
- Molecular Informatics (29 papers)Angewandte Chemie International Edition (29 papers)ChemMedChem (19 papers)
- Partner nations
- SwitzerlandGermanyAustria
In The Last Decade
Gisbert Schneider
446 papers receiving 22.2k citations
Hit Papers
Peers
Comparison fields: 5 of 211
- Computational Theory and Mathematics 10.2k
- Microbiology 2.1k
- Molecular Biology 13.3k
- Pharmacology 2.9k
- Organic Chemistry 3.3k
Countries citing papers authored by Gisbert Schneider
This map shows the geographic impact of Gisbert Schneider'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 Gisbert Schneider with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gisbert Schneider more than expected).
Fields of papers citing papers by Gisbert Schneider
This network shows the impact of papers produced by Gisbert Schneider. 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 Gisbert Schneider. The network helps show where Gisbert Schneider may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Gisbert Schneider, 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 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2024 | 11 | |
| 4 | 2024 | 6 | |
| 5 | 2024 | 44 | |
| 6 | 2024 | 4 | |
| 7 | 2023 | 11 | |
| 8 | 2023 | 11 | |
| 9 | 2023 | 50 | |
| 10 | Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSARbreakdown → | 2023 | 145 |
| 11 | 2023 | 8 | |
| 12 | 2021 | 3 | |
| 13 | 2019 | 3 | |
| 14 | 2014 | 43 | |
| 15 | 2010 | 246 | |
| 16 | 2010 | 4 | |
| 17 | 2009 | 70 | |
| 18 | 2008 | 61 | |
| 19 | 2004 | 17 | |
| 20 | 2000 | 111 |
About Gisbert Schneider
Gisbert Schneider is a scholar working on Computational Theory and Mathematics, Molecular Biology and Pharmacology, having authored 453 papers that have together received 22.9k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (238 papers), Chemical Synthesis and Analysis (99 papers), Microbial Natural Products and Biosynthesis (51 papers), Machine Learning in Materials Science (49 papers), Protein Structure and Dynamics (46 papers), Machine Learning in Bioinformatics (42 papers), Receptor Mechanisms and Signaling (38 papers) and RNA and protein synthesis mechanisms (34 papers). The work is most often cited by research in Computational Theory and Mathematics (10.2k citations), Microbiology (2.1k citations) and Molecular Biology (13.3k citations). Gisbert Schneider has collaborated with scholars based in Switzerland, Germany and Austria. Frequent co-authors include Petra Schneider, Jan A. Hiss, Uli Fechner, Daniel Reker, Tiago Rodrigues, Christopher D. Fjell, Robert E. W. Hancock, Francesca Grisoni, Paul Wrede and Evgeny Byvatov. Their work appears in journals such as Molecular Informatics, Angewandte Chemie International Edition, ChemMedChem, Journal of Chemical Information and Modeling and ChemBioChem.
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.