Arvind Ramanathan

4.1k total citations
130 papers, 1.8k citations indexed

About

Arvind Ramanathan is a scholar working on Molecular Biology, Artificial Intelligence and Epidemiology. According to data from OpenAlex, Arvind Ramanathan has authored 130 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 64 papers in Molecular Biology, 20 papers in Artificial Intelligence and 15 papers in Epidemiology. Recurrent topics in Arvind Ramanathan's work include Protein Structure and Dynamics (29 papers), Data-Driven Disease Surveillance (9 papers) and Enzyme Structure and Function (8 papers). Arvind Ramanathan is often cited by papers focused on Protein Structure and Dynamics (29 papers), Data-Driven Disease Surveillance (9 papers) and Enzyme Structure and Function (8 papers). Arvind Ramanathan collaborates with scholars based in United States, India and United Kingdom. Arvind Ramanathan's co-authors include Pratul K. Agarwal, Chakra S. Chennubhotla, Andrej Savol, Jason B. Castro, Christopher J. Langmead, Shang Gao, Laura Pullum, Georgia D. Tourassi, M. Todd Young and Debsindhu Bhowmik and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nucleic Acids Research.

In The Last Decade

Arvind Ramanathan

119 papers receiving 1.8k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Arvind Ramanathan United States	26	967	260	185	180	139	130	1.8k
Mike Tyka United States	5	903 0.9×	170 0.7×	122 0.7×	132 0.7×	51 0.4×	5	1.8k
Chen Cao China	26	1.4k 1.5×	208 0.8×	129 0.7×	102 0.6×	323 2.3×	101	3.2k
Edmund J. Crampin New Zealand	31	2.1k 2.2×	214 0.8×	143 0.8×	72 0.4×	529 3.8×	119	3.6k
Takashi Ishida Japan	17	1.7k 1.7×	337 1.3×	182 1.0×	106 0.6×	76 0.5×	70	2.2k
Dariusz Plewczyński Poland	28	1.9k 2.0×	208 0.8×	602 3.3×	155 0.9×	70 0.5×	163	2.9k
Raquel Norel United States	22	2.5k 2.5×	627 2.4×	452 2.4×	93 0.5×	209 1.5×	55	3.7k
Alex Gutteridge United Kingdom	22	1.9k 2.0×	307 1.2×	866 4.7×	68 0.4×	128 0.9×	31	2.5k
Barbora Kozlíková Czechia	16	1.4k 1.5×	300 1.2×	192 1.0×	49 0.3×	114 0.8×	64	2.1k
Wei Zheng China	31	2.0k 2.1×	426 1.6×	261 1.4×	62 0.3×	180 1.3×	96	3.4k

Countries citing papers authored by Arvind Ramanathan

Since Specialization

Citations

This map shows the geographic impact of Arvind Ramanathan'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 Arvind Ramanathan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arvind Ramanathan more than expected).

Fields of papers citing papers by Arvind Ramanathan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Arvind Ramanathan. 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 Arvind Ramanathan. The network helps show where Arvind Ramanathan may publish in the future.

Co-authorship network of co-authors of Arvind Ramanathan

This figure shows the co-authorship network connecting the top 25 collaborators of Arvind Ramanathan. A scholar is included among the top collaborators of Arvind Ramanathan 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 Arvind Ramanathan. Arvind Ramanathan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Underwood, Robert, Sandeep Madireddy, Franck Cappello, et al.. (2025). Automated MCQA Benchmarking at Scale: Evaluating Reasoning Traces as Retrieval Sources for Domain Adaptation of Small Language Models. 545–552.

Ma, Heng, et al.. (2025). LangChain-Parsl: Connect Large Language Model Agents to High Performance Computing Resource. 78–85.

Brace, Alexander, et al.. (2024). High Performance Binding Affinity Prediction with a Transformer-Based Surrogate Model. 571–580. 1 indexed citations

Ramanathan, Arvind, et al.. (2024). 7386 GlycA as a Novel Biomarker of Systemic Inflammation: Determinants and Implications in South Asians. Journal of the Endocrine Society. 8(Supplement_1).

Kim, Youngchang, N. Maltseva, C. Tesar, et al.. (2024). Epitopes recognition of SARS-CoV-2 nucleocapsid RNA binding domain by human monoclonal antibodies. iScience. 27(2). 108976–108976. 1 indexed citations

Phan, Lon, et al.. (2024). The evolution of dbSNP: 25 years of impact in genomic research. Nucleic Acids Research. 53(D1). D925–D931. 26 indexed citations

Clyde, Austin, et al.. (2023). ChemoGraph: Interactive Visual Exploration of the Chemical Space. Computer Graphics Forum. 42(3). 13–24. 2 indexed citations

Ward, Logan, Heng Ma, Murali Emani, et al.. (2023). Protein Generation via Genome-scale Language Models with Bio-physical Scoring. 95–101. 3 indexed citations

Kneller, Daniel W., Hui Li, Stephanie Galanie, et al.. (2021). Structural, Electronic, and Electrostatic Determinants for Inhibitor Binding to Subsites S1 and S2 in SARS-CoV-2 Main Protease. Journal of Medicinal Chemistry. 64(23). 17366–17383. 38 indexed citations

10.

Moin, Syed Tarique, Alessandro Pandini, Junqi Yin, et al.. (2021). Dynamic Profiling of β-Coronavirus 3CL M^pro Protease Ligand-Binding Sites. Journal of Chemical Information and Modeling. 61(6). 3058–3073. 35 indexed citations

11.

Heinrich, Frank, et al.. (2020). Steering Molecular Dynamics Simulations of Membrane-Associated Proteins with Neutron Reflection Results. Journal of Chemical Theory and Computation. 16(5). 3408–3419. 6 indexed citations

12.

Ramanathan, Arvind, Debsindhu Bhowmik, Heng Ma, M. Todd Young, & Christopher B. Stanley. (2019). Towards Exascale Bio-molecular Simulations with Artificial Intelligence Workflows. Bulletin of the American Physical Society. 2019. 1 indexed citations

13.

Duff, Michael R., Jose M. Borreguero, M.J. Cuneo, et al.. (2018). Modulating Enzyme Activity by Altering Protein Dynamics with Solvent. Biochemistry. 57(29). 4263–4275. 30 indexed citations

14.

Ramanathan, Arvind, et al.. (2017). Real-Time Multichannel Interfacing for a Dynamic Flat-Panel Audio Display Using the MATLAB Audio Systems Toolbox. Journal of the Audio Engineering Society. 3 indexed citations

15.

Jha, Sumit Kumar, et al.. (2017). Work-in-progress: testing autonomous cyber-physical systems using fuzzing features from convolutional neural networks. Journal of International Crisis and Risk Communication Research. 2 indexed citations

16.

Ramanathan, Arvind, et al.. (2016). Folding propensity of intrinsically disordered proteins by osmotic stress. Molecular BioSystems. 12(12). 3695–3701. 14 indexed citations

17.

Ramanathan, Arvind, et al.. (2013). Statistical Inference for Big Data Problems in Molecular Biophysics. 1 indexed citations

18.

Venkatesan, Aravind, et al.. (2012). In Search of Mutations of MSX1 Gene in Indian Nonsyndromic Cleft Palate Patients. The Journal of Indian Orthodontic Society. 46(1). 43–47. 1 indexed citations

19.

Ramanathan, Arvind, et al.. (2012). Common Docking Domain Mutation E322K of the ERK2 Gene is Infrequent in Oral Squamous Cell Carcinomas. Asian Pacific Journal of Cancer Prevention. 13(12). 6155–6157. 6 indexed citations

20.

Ramanathan, Arvind, et al.. (1989). Amarakośa : with the unpublished South Indian commentaries.

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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