Ram Samudrala
- Endocrinology top 2%
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods 26
- Molecular Biology top 2%
- Protein Structure and Dynamics 47
- RNA and protein synthesis mechanisms 29
- Machine Learning in Bioinformatics 22
- Bioinformatics and Genomic Networks 17
- Periodontics top 2%
- Virology top 5%
- HIV Research and Treatment 10
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- Enzyme Structure and Function 26
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- HIV/AIDS drug development and treatment 10
- Co-authors
- John MoultEkachai JenwitheesukEnoch S. HuangMichael LevittJason McDermottAaron D. GoldmanMichael D. LevittKai Wang
- Journals
- Proteins Structure Function and Bioinformatics (8 papers)Molecules (5 papers)Bioinformatics (5 papers)
- Partner nations
- United StatesThailandChina
In The Last Decade
Ram Samudrala
127 papers receiving 4.6k citations
Hit Papers
Peers
Comparison fields: 5 of 165
- Endocrinology 271
- Computational Theory and Mathematics 831
- Molecular Biology 3.2k
- Periodontics 173
- Virology 175
Countries citing papers authored by Ram Samudrala
This map shows the geographic impact of Ram Samudrala'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 Ram Samudrala with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ram Samudrala more than expected).
Fields of papers citing papers by Ram Samudrala
This network shows the impact of papers produced by Ram Samudrala. 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 Ram Samudrala. The network helps show where Ram Samudrala may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ram Samudrala, 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 | 2024 | 3 | |
| 2 | 2022 | 2 | |
| 3 | 2021 | 12 | |
| 4 | 2020 | 1 | |
| 5 | Foundations for a Realism-based Drug Repurposing Ontology. | 2019 | 3 |
| 6 | 2013 | 3 | |
| 7 | 2011 | 91 | |
| 8 | 2010 | 8 | |
| 9 | 2010 | 396 | |
| 10 | 2010 | 26 | |
| 11 | 2009 | 19 | |
| 12 | 2007 | 54 | |
| 13 | 2005 | 67 | |
| 14 | 2005 | 87 | |
| 15 | 2005 | 18 | |
| 16 | 2005 | 21 | |
| 17 | 2003 | 37 | |
| 18 | 2002 | 126 | |
| 19 | 2002 | 44 | |
| 20 | Philosophie de la musique libre | 2000 | 3 |
About Ram Samudrala
Ram Samudrala is a scholar working on Virology, Computational Theory and Mathematics, Molecular Biology, Periodontics and Infectious Diseases, having authored 127 papers that have together received 4.7k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (47 papers), RNA and protein synthesis mechanisms (29 papers), Enzyme Structure and Function (26 papers), Computational Drug Discovery Methods (26 papers), Machine Learning in Bioinformatics (22 papers), Bioinformatics and Genomic Networks (17 papers), HIV Research and Treatment (10 papers) and HIV/AIDS drug development and treatment (10 papers). The work is most often cited by research in Endocrinology (271 citations), Computational Theory and Mathematics (831 citations), Molecular Biology (3.2k citations), Periodontics (173 citations) and Virology (175 citations). Ram Samudrala has collaborated with scholars based in United States, Thailand and China. Frequent co-authors include John Moult, Ekachai Jenwitheesuk, Enoch S. Huang, Michael Levitt, Jason McDermott, Aaron D. Goldman, Michael D. Levitt, Kai Wang, Yu Xia and Keiji Murakami. Their work appears in journals such as Proteins Structure Function and Bioinformatics, Molecules, Bioinformatics, Nucleic Acids Research and Antiviral Therapy.
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.