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
- Histone Deacetylase Inhibitors Research 5
- Ubiquitin and proteasome pathways 5
- Epigenetics and DNA Methylation 2
- Oncology 7
- Peptidase Inhibition and Analysis 4
- Co-authors
- Xin Xu (7 shared papers)Pranav Shah (7 shared papers)Jian Jin (10 shared papers)H. Ümit Kanıskan (6 shared papers)Elias Carvalho Padilha (5 shared papers)Amy Q. Wang (3 shared papers)Mark J. Henderson (2 shared papers)Ning Sun (2 shared papers)
- Journals
- Journal of Medicinal Chemistry (7 papers)Bioorganic & Medicinal Chemistry (2 papers)Frontiers in Pharmacology (2 papers)Advanced Science (2 papers)Chemical Society Reviews (1 paper)
- Partner nations
- United StatesIndiaCanada
In The Last Decade
Md Kabir
19 papers receiving 352 citations
Peers
Comparison fields: 5 of 76
- Computational Theory and Mathematics 69
- Molecular Biology 241
- Oncology 89
- Pharmacology 24
- 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 | 71 | |
| 2 | 2023 | 34 | |
| 3 | 2022 | 32 | |
| 4 | 2020 | 29 | |
| 5 | 2019 | 25 | |
| 6 | 2020 | 25 | |
| 7 | 2022 | 22 | |
| 8 | 2023 | 18 | |
| 9 | 2023 | 17 | |
| 10 | 2014 | 14 | |
| 11 | 2016 | 14 | |
| 12 | 2023 | 13 | |
| 13 | 2022 | 9 | |
| 14 | 2023 | 9 | |
| 15 | 2024 | 8 | |
| 16 | 2024 | 6 | |
| 17 | 2024 | 4 | |
| 18 | 2024 | 2 | |
| 19 | 2024 | 1 | |
| 20 | 2025 | 0 |
About Md Kabir
Md Kabir is a scholar working on Molecular Biology, Oncology, Computational Theory and Mathematics, Pharmacology and Spectroscopy, having authored 20 papers that have together received 353 indexed citations. Recurring topics across this work include Protein Degradation and Inhibitors (9 papers), Histone Deacetylase Inhibitors Research (5 papers), Computational Drug Discovery Methods (5 papers), Ubiquitin and proteasome pathways (5 papers), Pharmacogenetics and Drug Metabolism (4 papers), Peptidase Inhibition and Analysis (4 papers), Analytical Chemistry and Chromatography (2 papers) and Epigenetics and DNA Methylation (2 papers). The work is most often cited by research in Computational Theory and Mathematics (69 citations), Molecular Biology (241 citations), Oncology (89 citations), Pharmacology (24 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 Xin Xu, Pranav Shah, Jian Jin, H. Ümit Kanıskan, Elias Carvalho Padilha, Amy Q. Wang, Mark J. Henderson, Ning Sun, Anton Simeonov and J. W. Williams. Their work appears in journals such as Journal of Medicinal Chemistry, Bioorganic & Medicinal Chemistry, Frontiers in Pharmacology, Advanced Science and Chemical Society Reviews.
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.