Natesh Singh

16 papers receiving 569 citations

Natesh Singh's Hit Papers

Drug discovery and development: introduction to the general public and patient groups 2023 · 98 citations
980+1+2Years since publication255075

Peers

Natesh Singh
Comparison fields: 5 of 86
  • Biochemistry 106
  • Computational Theory and Mathematics 150
  • Molecular Biology 371
  • Oncology 112
  • Hematology 45
Replace Giovanni Cianchetta with:
Giovanni Cianchetta United States
Kevin N. Dack United Kingdom
Eric S. Marr United States
Cornelia Bellamacina United States
Xiaoke Guo China
Thomas Pauly United States
Norbert Furtmann Germany
Tímea Polgár Hungary
Scott Martin United Kingdom
Tomomi Noguchi‐Yachide Japan
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Citations per field
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Citations per year

Countries citing papers authored by Natesh Singh

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Natesh Singh Line = papers co-authored together Natesh Singh links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 2018132
2 2018109
3
Drug discovery and development: introduction to the general public and patient groups
Hit paper breakdown →
202398
4 202076
5 201841
6 202040
7 201923
8 202016
9 202115
10 202211
11 20225
12 20204
13 20224
14 20204
15 20222
16 20162

About Natesh Singh

Natesh Singh is a scholar working on Molecular Biology, Computational Theory and Mathematics, Infectious Diseases, Biochemistry and Immunology, having authored 16 papers that have together received 582 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (9 papers), Protein Structure and Dynamics (3 papers), Amino Acid Enzymes and Metabolism (3 papers), Microbial Natural Products and Biosynthesis (2 papers), Epigenetics and DNA Methylation (2 papers), Chemical Synthesis and Analysis (2 papers), SARS-CoV-2 and COVID-19 Research (2 papers) and Drug Transport and Resistance Mechanisms (2 papers). The work is most often cited by research in Biochemistry (106 citations), Computational Theory and Mathematics (150 citations), Molecular Biology (371 citations), Oncology (112 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, Jean‐Luc Poyet, Philippe Vayer, Étienne Decroly, Abdel‐Majid Khatib, Katja Parapatics and Georg E. Winter. Their work appears in journals such as International Journal of Molecular Sciences, Scientific Reports, European Journal of Pharmaceutical Sciences, Computational and Structural Biotechnology Journal and Drug Discovery Today.

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.

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