Thirumurthy Madhavan

934 total citations
72 papers, 694 citations indexed

About

Thirumurthy Madhavan is a scholar working on Molecular Biology, Computational Theory and Mathematics and Oncology. According to data from OpenAlex, Thirumurthy Madhavan has authored 72 papers receiving a total of 694 indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Molecular Biology, 37 papers in Computational Theory and Mathematics and 16 papers in Oncology. Recurrent topics in Thirumurthy Madhavan's work include Computational Drug Discovery Methods (37 papers), Monoclonal and Polyclonal Antibodies Research (11 papers) and Synthesis and biological activity (10 papers). Thirumurthy Madhavan is often cited by papers focused on Computational Drug Discovery Methods (37 papers), Monoclonal and Polyclonal Antibodies Research (11 papers) and Synthesis and biological activity (10 papers). Thirumurthy Madhavan collaborates with scholars based in India, South Korea and United States. Thirumurthy Madhavan's co-authors include Gugan Kothandan, Changdev G. Gadhe, Seung Joo Cho, Honglae Sohn, Palaniyandi Velusamy, S. Periyar Selvam, Hyun Woo Park, Young-Jun Jeon, Balachandran Manavalan and Cheol Hee Choi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and European Journal of Pharmacology.

In The Last Decade

Thirumurthy Madhavan

61 papers receiving 671 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thirumurthy Madhavan India 13 289 193 96 90 88 72 694
Kumari Sunita Prajapati India 15 353 1.2× 183 0.9× 75 0.8× 51 0.6× 110 1.3× 37 716
Asif Shahriar Bangladesh 9 326 1.1× 92 0.5× 81 0.8× 66 0.7× 143 1.6× 20 766
Khattab Al-Khafaji Türkiye 15 263 0.9× 206 1.1× 125 1.3× 39 0.4× 36 0.4× 32 648
Shashanka K. Prasad India 17 204 0.7× 127 0.7× 74 0.8× 53 0.6× 57 0.6× 76 820
Vinay Kumar India 20 379 1.3× 384 2.0× 193 2.0× 61 0.7× 60 0.7× 57 1.0k
Lekshmi R. Nath India 18 353 1.2× 76 0.4× 125 1.3× 55 0.6× 128 1.5× 65 986
Prem Prakash Kushwaha India 19 453 1.6× 200 1.0× 108 1.1× 44 0.5× 148 1.7× 43 983
Wafa Ali Eltayb Sudan 14 328 1.1× 135 0.7× 127 1.3× 99 1.1× 40 0.5× 40 854
Madhu Chopra India 19 396 1.4× 101 0.5× 137 1.4× 30 0.3× 107 1.2× 57 865
Thet Thet Htar Malaysia 18 229 0.8× 71 0.4× 153 1.6× 77 0.9× 55 0.6× 41 671

Countries citing papers authored by Thirumurthy Madhavan

Since Specialization
Citations

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

Fields of papers citing papers by Thirumurthy Madhavan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thirumurthy Madhavan

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

All Works

20 of 20 papers shown
1.
Sharma, Aditi, et al.. (2024). ANN multi-layer perceptron for prediction of blood–brain barrier permeable compounds for central nervous system therapeutics. Journal of Biomolecular Structure and Dynamics. 43(16). 9011–9016. 2 indexed citations
2.
Boreddy, Srinivas Reddy, et al.. (2024). Functional characterization of complete and immunodominant epitopes of a novel pollen allergen from Parthenium hysterophorus. European Annals of Allergy and Clinical Immunology. 57(6). 260–260.
3.
Sohn, Honglae, et al.. (2024). AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules. Pharmaceuticals. 17(12). 1693–1693. 3 indexed citations
4.
Madhavan, Thirumurthy, et al.. (2024). In-silico binding affinity of a phage display library screened novel peptide against various FABPs. In Silico Pharmacology. 12(2). 76–76. 2 indexed citations
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Madhavan, Thirumurthy, et al.. (2023). Computational study of the motor neuron protein KIF5A to identify nsSNPs, bioactive compounds, and its key regulators. Frontiers in Genetics. 14. 1282234–1282234. 3 indexed citations
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Madhavan, Thirumurthy, et al.. (2022). Morin inhibits colon cancer stem cells by inhibiting PUM1 expression in vitro. Medical Oncology. 39(12). 251–251. 13 indexed citations
10.
11.
Yadalam, Pradeep Kumar, et al.. (2021). Antiviral Essential Oil Components Against SARS-CoV-2 in Pre-procedural Mouth Rinses for Dental Settings During COVID-19: A Computational Study. Frontiers in Chemistry. 9. 642026–642026. 27 indexed citations
12.
Madhavan, Thirumurthy, et al.. (2019). Understanding the structural features of JAK2 inhibitors: a combined 3D-QSAR, DFT and molecular dynamics study. Molecular Diversity. 23(4). 845–874. 12 indexed citations
13.
Thirumavalavan, Munusamy, et al.. (2018). Isolation, purification and characterization of proteinaceous fungal α-amylase inhibitor from rhizome of Cheilocostus speciosus (J.Koenig) C.D.Specht. International Journal of Biological Macromolecules. 111. 39–51. 8 indexed citations
14.
Lee, Sung Haeng, et al.. (2016). Structural characterization of human CRTh2: a combined homology modeling, molecular docking and 3D-QSAR-based in silico approach. Medicinal Chemistry Research. 25(4). 653–671. 4 indexed citations
15.
Singh, Abhay K., Rajinder Parshad, Thirumurthy Madhavan, et al.. (2011). Prognostic Significance of Cyclooxygenase-2 and Response to Chemotherapy in Invasive Ductal Breast Carcinoma Patients by Real Time Surface Plasmon Resonance Analysis. DNA and Cell Biology. 30(10). 801–807. 7 indexed citations
16.
Gadhe, Changdev G., Thirumurthy Madhavan, Gugan Kothandan, & Seung Joo Cho. (2011). In Silico Quantitative Structure-Activity Relationship Studies on P-gp Modulators of Tetrahydroisoquinoline-Ethyl-Phenylamine Series. BMC Structural Biology. 11(1). 5–5. 21 indexed citations
17.
Gadhe, Changdev G., et al.. (2011). Various Partial Charge Schemes on 3D-QSAR Models for P-gp Inhibiting Adamantyl Derivatives. Bulletin of the Korean Chemical Society. 32(5). 1604–1612. 4 indexed citations
18.
Madhavan, Thirumurthy, et al.. (2011). Various atomic charge calculation schemes of CoMFA on HIF‐1 inhibitors of moracin analogs. International Journal of Quantum Chemistry. 112(4). 995–1005. 8 indexed citations
19.
Kothandan, Gugan, Changdev G. Gadhe, Thirumurthy Madhavan, Cheol Hee Choi, & Seung Joo Cho. (2011). Docking and 3D-QSAR (quantitative structure activity relationship) studies of flavones, the potent inhibitors of p-glycoprotein targeting the nucleotide binding domain. European Journal of Medicinal Chemistry. 46(9). 4078–4088. 47 indexed citations
20.
Kothandan, Gugan, Thirumurthy Madhavan, Changdev G. Gadhe, & Seung Joo Cho. (2010). Pseudoreceptor: Concept and an Overview. 3(3). 162–167. 6 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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