Natesh Singh
Impact in
- Biochemistry top 5%
- Amino Acid Enzymes and Metabolism
-
- Computational Drug Discovery Methods
Papers in
-
- Protein Structure and Dynamics 2
- Epigenetics and DNA Methylation 2
-
- Computational Drug Discovery Methods 8
- Co-authors
- Bruno O. Villoutreix (12 shared papers)Gerhard F. Ecker (4 shared papers)Ludovic Chaput (4 shared papers)Katya Tsaioun (2 shared papers)Philippe Vayer (2 shared papers)Jean‐Luc Poyet (1 shared paper)Martin G. Jaeger (1 shared paper)André C. Müller (1 shared paper)
- Journals
- International Journal of Molecular Sciences (3 papers)Briefings in Bioinformatics (2 papers)Bioinformatics (1 paper)Drug Discovery Today (1 paper)European Journal of Pharmaceutical Sciences (1 paper)
- Partner nations
- FranceAustriaUnited States
In The Last Decade
Natesh Singh
17 papers receiving 609 citations
Natesh Singh's Hit Papers
Peers
Comparison fields: 5 of 85
- Biochemistry 107
- Computational Theory and Mathematics 147
- Molecular Biology 360
- Oncology 114
- Hematology 45
Countries citing papers authored by Natesh Singh
This map shows the geographic impact of Natesh Singh'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 Natesh Singh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Natesh Singh more than expected).
Fields of papers citing papers by Natesh Singh
This network shows the impact of papers produced by Natesh Singh. 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 Natesh Singh. The network helps show where Natesh Singh may publish in the future.
Co-authors
The 25 scholars most cited alongside Natesh Singh, 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 | 2018 | 134 | |
| 2 | Drug discovery and development: introduction to the general public and patient groups Hit paper breakdown → | 2023 | 115 |
| 3 | 2018 | 112 | |
| 4 | 2020 | 82 | |
| 5 | 2018 | 42 | |
| 6 | 2020 | 41 | |
| 7 | 2019 | 23 | |
| 8 | 2020 | 17 | |
| 9 | 2021 | 15 | |
| 10 | 2022 | 11 | |
| 11 | 2022 | 6 | |
| 12 | 2020 | 6 | |
| 13 | 2020 | 5 | |
| 14 | 2022 | 5 | |
| 15 | 2022 | 2 | |
| 16 | 2016 | 2 | |
| 17 | 2025 | 1 |
About Natesh Singh
Natesh Singh is a scholar working on Molecular Biology, Computational Theory and Mathematics, Infectious Diseases, Biochemistry and Immunology, having authored 17 papers that have together received 619 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (8 papers), Amino Acid Enzymes and Metabolism (3 papers), Drug Transport and Resistance Mechanisms (2 papers), Protein Structure and Dynamics (2 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Epigenetics and DNA Methylation (2 papers), Click Chemistry and Applications (1 paper) and Biomarkers in Disease Mechanisms (1 paper). The work is most often cited by research in Biochemistry (107 citations), Computational Theory and Mathematics (147 citations), Molecular Biology (360 citations), Oncology (114 citations) and Hematology (45 citations). Natesh Singh has collaborated with scholars based in France, Austria and United States. Frequent co-authors include Bruno O. Villoutreix, Gerhard F. Ecker, Ludovic Chaput, Katya Tsaioun, Philippe Vayer, Jean‐Luc Poyet, Martin G. Jaeger, André C. Müller, Abdel‐Majid Khatib and Sophie Bauer. Their work appears in journals such as International Journal of Molecular Sciences, Briefings in Bioinformatics, Bioinformatics, Drug Discovery Today and European Journal of Pharmaceutical Sciences.
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.