Yogesh Sabnis
- Molecular Biology
- Organic Chemistry top 5%
- Computational Theory and Mathematics top 2%
- Oncology
- Public Health, Environmental and Occupational Health top 10%
- Co-authors
- Lyn H. JonesTinghu ZhangQingsong LiuNathanael S. GraySara J. BuhrlageZheng ZhaoMitchell A. AveryPhilip J. Rosenthal
- Topics
- Computational Drug Discovery Methods (8 papers)HIV/AIDS drug development and treatment (8 papers)Synthesis and biological activity (3 papers)
- Partner nations
- United StatesSwedenUnited Kingdom
In The Last Decade
Yogesh Sabnis
20 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 88
- Molecular Biology 654
- Organic Chemistry 502
- Computational Theory and Mathematics 296
- Oncology 204
- Public Health, Environmental and Occupational Health 179
Countries citing papers authored by Yogesh Sabnis
This map shows the geographic impact of Yogesh Sabnis'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 Yogesh Sabnis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yogesh Sabnis more than expected).
Fields of papers citing papers by Yogesh Sabnis
This network shows the impact of papers produced by Yogesh Sabnis. 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 Yogesh Sabnis. The network helps show where Yogesh Sabnis may publish in the future.
Co-authorship network of co-authors of Yogesh Sabnis
This figure shows the co-authorship network connecting the top 25 collaborators of Yogesh Sabnis. A scholar is included among the top collaborators of Yogesh Sabnis 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 Yogesh Sabnis. Yogesh Sabnis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 35 | |
| 2 | 44 | |
| 3 | Developing Irreversible Inhibitors of the Protein Kinase Cysteinomebreakdown → | 513 |
| 4 | 17 | |
| 5 | 44 | |
| 6 | 28 | |
| 7 | 45 | |
| 8 | 47 | |
| 9 | 41 | |
| 10 | 23 | |
| 11 | 44 | |
| 12 | 30 | |
| 13 | 14 | |
| 14 | 85 | |
| 15 | 41 | |
| 16 | 46 | |
| 17 | 45 | |
| 18 | 35 | |
| 19 | 10 | |
| 20 | 55 |
About Yogesh Sabnis
Yogesh Sabnis is a scholar working on Computational Theory and Mathematics, Pharmacology and Infectious Diseases, having authored 20 papers that have together received 1.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (8 papers), HIV/AIDS drug development and treatment (8 papers) and Synthesis and biological activity (3 papers). The work is most often cited by research in Computational Theory and Mathematics (296 citations), Organic Chemistry (502 citations) and Molecular Biology (654 citations). Yogesh Sabnis has collaborated with scholars based in United States, Sweden and United Kingdom. Frequent co-authors include Lyn H. Jones, Tinghu Zhang, Qingsong Liu, Nathanael S. Gray, Sara J. Buhrlage, Zheng Zhao, Mitchell A. Avery, Philip J. Rosenthal, Prashant Desai and Anders Karlén. Their work appears in journals such as Journal of Medicinal Chemistry, Tetrahedron and 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.