Md Kabir
Impact in
-
- Computational Drug Discovery Methods
-
- Protein Degradation and Inhibitors
- Histone Deacetylase Inhibitors Research
- Ubiquitin and proteasome pathways
- Epigenetics and DNA Methylation
Papers in
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- Protein Degradation and Inhibitors 9
- Ubiquitin and proteasome pathways 5
- Histone Deacetylase Inhibitors Research 5
- Epigenetics and DNA Methylation 2
- Oncology 7
- Peptidase Inhibition and Analysis 4
- Co-authors
- Jian Jin (10 shared papers)Pranav Shah (7 shared papers)Xin Xu (7 shared papers)H. Ümit Kanıskan (6 shared papers)Elias Carvalho Padilha (5 shared papers)Amy Q. Wang (3 shared papers)Vishal B. Siramshetty (3 shared papers)J. W. Williams (3 shared papers)
- Journals
- Journal of Medicinal Chemistry (7 papers)Advanced Science (2 papers)Bioorganic & Medicinal Chemistry (2 papers)Frontiers in Pharmacology (2 papers)Scientific Reports (1 paper)
- Partner nations
- United StatesIndiaCanada
In The Last Decade
Md Kabir
18 papers receiving 383 citations
Peers
Comparison fields: 5 of 75
- Computational Theory and Mathematics 69
- Molecular Biology 253
- Oncology 89
- Pharmacology 23
- Nuclear Energy and Engineering 1
Countries citing papers authored by Md Kabir
This map shows the geographic impact of Md Kabir'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 Md Kabir with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Md Kabir more than expected).
Fields of papers citing papers by Md Kabir
This network shows the impact of papers produced by Md Kabir. 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 Md Kabir. The network helps show where Md Kabir may publish in the future.
Co-authors
The 25 scholars most cited alongside Md Kabir, 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 | 2020 | 75 | |
| 2 | 2023 | 41 | |
| 3 | 2022 | 34 | |
| 4 | 2020 | 30 | |
| 5 | 2020 | 28 | |
| 6 | 2022 | 27 | |
| 7 | 2019 | 26 | |
| 8 | 2023 | 20 | |
| 9 | 2023 | 18 | |
| 10 | 2023 | 15 | |
| 11 | 2014 | 14 | |
| 12 | 2016 | 14 | |
| 13 | 2023 | 10 | |
| 14 | 2022 | 9 | |
| 15 | 2024 | 8 | |
| 16 | 2024 | 7 | |
| 17 | 2024 | 5 | |
| 18 | 2024 | 2 | |
| 19 | 2025 | 0 |
About Md Kabir
Md Kabir is a scholar working on Molecular Biology, Oncology, Computational Theory and Mathematics, Pharmacology and Spectroscopy, having authored 19 papers that have together received 383 indexed citations. Recurring topics across this work include Protein Degradation and Inhibitors (9 papers), Ubiquitin and proteasome pathways (5 papers), Histone Deacetylase Inhibitors Research (5 papers), Computational Drug Discovery Methods (5 papers), Pharmacogenetics and Drug Metabolism (4 papers), Peptidase Inhibition and Analysis (4 papers), Epigenetics and DNA Methylation (2 papers) and Statistical Methods in Clinical Trials (2 papers). The work is most often cited by research in Computational Theory and Mathematics (69 citations), Molecular Biology (253 citations), Oncology (89 citations), Pharmacology (23 citations) and Nuclear Energy and Engineering (1 citation). Md Kabir has collaborated with scholars based in United States, India and Canada. Frequent co-authors include Jian Jin, Pranav Shah, Xin Xu, H. Ümit Kanıskan, Elias Carvalho Padilha, Amy Q. Wang, Vishal B. Siramshetty, J. W. Williams, Ning Sun and Anton Simeonov. Their work appears in journals such as Journal of Medicinal Chemistry, Advanced Science, Bioorganic & Medicinal Chemistry, Frontiers in Pharmacology and Scientific Reports.
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.