Devadasan Velmurugan

1.4k total citations
73 papers, 1.1k citations indexed

About

Devadasan Velmurugan is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Devadasan Velmurugan has authored 73 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Molecular Biology, 17 papers in Computational Theory and Mathematics and 15 papers in Organic Chemistry. Recurrent topics in Devadasan Velmurugan's work include Computational Drug Discovery Methods (17 papers), Protein Structure and Dynamics (9 papers) and Venomous Animal Envenomation and Studies (9 papers). Devadasan Velmurugan is often cited by papers focused on Computational Drug Discovery Methods (17 papers), Protein Structure and Dynamics (9 papers) and Venomous Animal Envenomation and Studies (9 papers). Devadasan Velmurugan collaborates with scholars based in India, Japan and United States. Devadasan Velmurugan's co-authors include M. Michael Gromiha, Dinesh Kumar Kesavan, Aparna Vasudevan, Waheeta Hopper, S. Anusuya, Vijayakumar Balakrishnan, C. V. Ramakrishnan, Shanmugavel Chinnathambi, Singaravelu Ganesan and R. Manjunatha Kini and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Biochemistry.

In The Last Decade

Devadasan Velmurugan

72 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Devadasan Velmurugan India	19	555	198	185	142	139	73	1.1k
Jieling Zhao United States	6	912 1.6×	205 1.0×	337 1.8×	91 0.6×	132 0.9×	7	1.7k
Yuan-Ling Xia China	6	683 1.2×	171 0.9×	300 1.6×	47 0.3×	90 0.6×	12	1.2k
Martiniano Bello Mexico	22	816 1.5×	220 1.1×	281 1.5×	70 0.5×	158 1.1×	99	1.4k
Mario S. Valdés‐Tresanco Colombia	8	1.1k 1.9×	338 1.7×	464 2.5×	63 0.4×	201 1.4×	11	2.0k
Sarah Naomi Bolz Germany	8	757 1.4×	249 1.3×	300 1.6×	44 0.3×	137 1.0×	12	1.4k
Floriano Paes Silva Brazil	24	962 1.7×	726 3.7×	505 2.7×	209 1.5×	121 0.9×	70	2.2k
Mario E. Valdés‐Tresanco Cuba	11	1.1k 1.9×	364 1.8×	467 2.5×	64 0.5×	241 1.7×	24	2.1k
Shailima Rampogu South Korea	20	524 0.9×	256 1.3×	334 1.8×	39 0.3×	111 0.8×	60	1.0k
Colin G. Ferguson United States	14	784 1.4×	181 0.9×	193 1.0×	30 0.2×	65 0.5×	19	1.3k
Edeildo Ferreira da Silva‐Júnior Brazil	23	451 0.8×	391 2.0×	188 1.0×	25 0.2×	111 0.8×	93	1.3k

Countries citing papers authored by Devadasan Velmurugan

Since Specialization

Citations

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

Fields of papers citing papers by Devadasan Velmurugan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Devadasan Velmurugan

This figure shows the co-authorship network connecting the top 25 collaborators of Devadasan Velmurugan. A scholar is included among the top collaborators of Devadasan Velmurugan 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 Devadasan Velmurugan. Devadasan Velmurugan 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

Thirumavalavan, Munusamy, et al.. (2025). Analytical insights on identification and characterization of potential bioactive compounds from two renal lithiasis-resistant plants viz. Aerva lanata and Aerva javanica. Microchemical Journal. 217. 114967–114967.

Dhanabalan, Anantha Krishnan, et al.. (2024). Identification of a novel drug molecule for neurodegenerative disease from marine algae through in-silico analysis. Journal of Biomolecular Structure and Dynamics. 43(16). 8883–8892. 3 indexed citations

Vaseeharan, Baskaralingam, Abdulaziz S. Alothaim, Esam S. Al‐Malki, et al.. (2023). Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors. Methods. 219. 82–94. 22 indexed citations

Joshi, Vikram, et al.. (2022). Syringol isolated from Eleusine coracana (L.) Gaertn bran suppresses inflammatory response through the down-regulation of cPLA2, COX-2, IκBα, p38 and MPO signaling in sPLA2 induced mice paw oedema. Inflammopharmacology. 30(5). 1853–1870. 4 indexed citations

Dhanabalan, Anantha Krishnan, et al.. (2021). Identification of a Chlorogenic Ester as a Monoamine Oxidase (MAO-B) Inhibitor by Integrating “Traditional and Machine Learning” Virtual Screening and In Vitro as well as In Vivo Validation: A Lead against Neurodegenerative Disorders?. ACS Chemical Neuroscience. 12(19). 3690–3707. 7 indexed citations

Selvakumar, R., Anantha Krishnan Dhanabalan, C. V. Ramakrishnan, Devadasan Velmurugan, & K. Gunasekaran. (2019). Identification of novel NAD(P)H dehydrogenase [quinone] 1 antagonist using computational approaches. Journal of Biomolecular Structure and Dynamics. 38(3). 682–696. 3 indexed citations

Vincent, Savariar, et al.. (2017). Bioinformatics approach to prioritize known drugs towards repurposing for tuberculosis. Medical Hypotheses. 103. 39–45. 10 indexed citations

Suvilesh, Kanve N., et al.. (2017). EC-PIII, a novel non-hemorrhagic procoagulant metalloproteinase: Purification and characterization from Indian Echis carinatus venom. International Journal of Biological Macromolecules. 106. 193–199. 9 indexed citations

Gupta, Ankita, et al.. (2016). Fasciola gigantica thioredoxin glutathione reductase: Biochemical properties and structural modeling. International Journal of Biological Macromolecules. 89. 152–160. 14 indexed citations

10.

Velmurugan, Devadasan, et al.. (2016). Docking-based virtual screening of known drugs against murE of Mycobacterium tuberculosis towards repurposing for TB. Bioinformation. 12(8). 368–372. 14 indexed citations

11.

Velmurugan, Devadasan, et al.. (2015). MOLECULAR MODELLING, QSAR AND PHARMACOPHORE STUDIES ON ANTI-VIRAL, ANTI-MALARIAL AND ANTI-INFLAMMATORY BIOACTIVE COMPOUNDS FROM MARINE SOURCES. Asian Journal of Pharmaceutical and Clinical Research. 8(3). 36–43. 4 indexed citations

12.

Urs, Ankanahalli N. Nanjaraj, C. V. Ramakrishnan, Vikram Joshi, et al.. (2015). Progressive Hemorrhage and Myotoxicity Induced by Echis carinatus Venom in Murine Model: Neutralization by Inhibitor Cocktail of N,N,N',N'-Tetrakis (2-Pyridylmethyl) Ethane-1,2-Diamine and Silymarin. PLoS ONE. 10(8). e0135843–e0135843. 15 indexed citations

13.

Vasudevan, Aparna, et al.. (2014). Identification of Natural Compound Inhibitors for Multidrug Efflux Pumps of Escherichia coli and Pseudomonas aeruginosa Using In Silico High-Throughput Virtual Screening and In Vitro Validation. PLoS ONE. 9(7). e101840–e101840. 102 indexed citations

14.

Devaraji, Vinod, et al.. (2014). Virtual Screening and Discovery of Novel Aurora Kinase Inhibitors. Current Topics in Medicinal Chemistry. 14(17). 2006–2019. 17 indexed citations

15.

Urs, Ankanahalli N. Nanjaraj, Yariswamy Manjunath, C. V. Ramakrishnan, et al.. (2014). Inhibitory potential of three zinc chelating agents against the proteolytic, hemorrhagic, and myotoxic activities of Echis carinatus venom. Toxicon. 93. 68–78. 24 indexed citations

16.

Velmurugan, Devadasan, et al.. (2013). Design and Docking Studies of Peptide Inhibitors as Potential Antiviral Drugs for Dengue Virus Ns2b/Ns3 Protease. Protein and Peptide Letters. 21(8). 815–827. 10 indexed citations

17.

Selvakumar, R., et al.. (2013). Exploring GpG bases next to anticodon in tRNA subsets. Bioinformation. 9(9). 466–470. 2 indexed citations

18.

Muralidharan, S., P. Nagapandiselvi, T. Srinivasan, R. Gopalakrishnan, & Devadasan Velmurugan. (2012). 3-Methyl-4-nitrophenol–4-dimethylaminopyridine (1/1). Acta Crystallographica Section E Structure Reports Online. 68(11). o3106–o3106. 4 indexed citations

19.

Gunasekaran, K., et al.. (2012). Structure based design of compounds from natural sources for diabetes and inflammation. Bioinformation. 8(23). 1125–1131. 7 indexed citations

20.

Sujatha, S., et al.. (2009). 3β-taraxerol of Mangifera indica, a PI3K dependent dual activator of glucose transport and glycogen synthesis in 3T3-L1 adipocytes. Biochimica et Biophysica Acta (BBA) - General Subjects. 1800(3). 359–366. 45 indexed citations

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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