Sivakumar Prasanth Kumar

968 total citations · 1 hit paper
57 papers, 685 citations indexed

About

Sivakumar Prasanth Kumar is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Sivakumar Prasanth Kumar has authored 57 papers receiving a total of 685 indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Molecular Biology, 29 papers in Computational Theory and Mathematics and 8 papers in Organic Chemistry. Recurrent topics in Sivakumar Prasanth Kumar's work include Computational Drug Discovery Methods (29 papers), Protein Structure and Dynamics (9 papers) and Synthesis and biological activity (5 papers). Sivakumar Prasanth Kumar is often cited by papers focused on Computational Drug Discovery Methods (29 papers), Protein Structure and Dynamics (9 papers) and Synthesis and biological activity (5 papers). Sivakumar Prasanth Kumar collaborates with scholars based in India, United States and United Arab Emirates. Sivakumar Prasanth Kumar's co-authors include Himanshu Pandya, Yogesh T. Jasrai, Chirag Patel, Rakesh Rawal, Prakash C. Jha, Thirumala‐Devi Kanneganti, Rebecca E. Tweedell, Balamurugan Sundaram, Saumya Patel and Krunal Modi and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and Immunity.

In The Last Decade

Sivakumar Prasanth Kumar

56 papers receiving 670 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The NLR family of innate immune and cell death sensors

2024 86 citations Rebecca E. Tweedell, Sivakumar Prasanth Kumar et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sivakumar Prasanth Kumar India	16	297	261	140	79	74	57	685
Clement Agoni South Africa	18	471 1.6×	229 0.9×	155 1.1×	96 1.2×	61 0.8×	74	924
Kiran Bharat Lokhande India	16	289 1.0×	191 0.7×	111 0.8×	94 1.2×	50 0.7×	62	653
Matthew L. Danielson United States	7	354 1.2×	168 0.6×	135 1.0×	49 0.6×	33 0.4×	7	737
Kowit Hengphasatporn Japan	14	188 0.6×	203 0.8×	122 0.9×	171 2.2×	42 0.6×	64	657
Rajendra Bhadane Finland	12	307 1.0×	238 0.9×	135 1.0×	106 1.3×	33 0.4×	25	726
Prem Prakash Kushwaha India	19	453 1.5×	200 0.8×	108 0.8×	155 2.0×	55 0.7×	43	983
Vijayakumar Gosu South Korea	12	512 1.7×	314 1.2×	121 0.9×	72 0.9×	191 2.6×	25	961
Muhammed Tılahun Muhammed Türkiye	13	326 1.1×	231 0.9×	296 2.1×	40 0.5×	33 0.4×	67	788
Rafael Ferreira Dantas Brazil	15	266 0.9×	154 0.6×	198 1.4×	44 0.6×	46 0.6×	30	711
Shahzaib Ahamad India	21	614 2.1×	209 0.8×	163 1.2×	128 1.6×	37 0.5×	47	1.1k

Countries citing papers authored by Sivakumar Prasanth Kumar

Since Specialization

Citations

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

Fields of papers citing papers by Sivakumar Prasanth Kumar

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sivakumar Prasanth Kumar. 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 Sivakumar Prasanth Kumar. The network helps show where Sivakumar Prasanth Kumar may publish in the future.

Co-authorship network of co-authors of Sivakumar Prasanth Kumar

This figure shows the co-authorship network connecting the top 25 collaborators of Sivakumar Prasanth Kumar. A scholar is included among the top collaborators of Sivakumar Prasanth Kumar 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 Sivakumar Prasanth Kumar. Sivakumar Prasanth Kumar 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

Qin, Qiang, Charles E. King, Sivakumar Prasanth Kumar, et al.. (2025). Identification of an IRF–ZBP1–caspase-8–NINJ1 axis in driving PANoptosis and pathology during alcohol-associated liver disease. Proceedings of the National Academy of Sciences. 122(50). e2525296122–e2525296122.

Patel, Saumya, et al.. (2023). Design of 2-amino-6-methyl-pyrimidine benzoic acids as ATP competitive casein kinase-2 (CK2) inhibitors using structure- and fragment-based design, docking and molecular dynamic simulation studies. SAR and QSAR in environmental research. 34(3). 211–230. 3 indexed citations

Pandya, Himanshu, et al.. (2023). Assessment of Cytotoxic Effects of Latex from Cascabela thevetia (L.) Lippold and Plumeria alba L. via In vitro and In silico Approaches. Journal of Natural Remedies. 89–104. 2 indexed citations

Kumar, Sivakumar Prasanth, et al.. (2023). Targeting leukemic stem cell subpopulation in AML using phytochemicals: An in-silico and in-vitro approach. Computers in Biology and Medicine. 155. 106644–106644. 2 indexed citations

Tweedell, Rebecca E., Sivakumar Prasanth Kumar, & Thirumala‐Devi Kanneganti. (2023). Innate sensing pathways: Defining new innate immune and inflammatory cell death pathways has shaped translational applications. PLoS Biology. 21(2). e3002022–e3002022. 5 indexed citations

Kumar, Vikas, Amr S. Abouzied, M. A. Bhat, et al.. (2023). Phytochemical Analysis, In Vitro Biological Activities, and Computer-Aided Analysis of Potentilla nepalensis Hook Compounds as Potential Melanoma Inhibitors Based on Molecular Docking, MD Simulations, and ADMET. Molecules. 28(13). 5108–5108. 6 indexed citations

Patel, Chirag, et al.. (2022). Computational investigation of natural compounds as potential main protease (Mpro) inhibitors for SARS-CoV-2 virus. Computers in Biology and Medicine. 151(Pt A). 106318–106318. 22 indexed citations

Kumar, Sivakumar Prasanth, et al.. (2022). MHC2AffyPred : A machine‐learning approach to estimate affinity of MHC class II peptides based on structural interaction fingerprints. Proteins Structure Function and Bioinformatics. 91(2). 277–289. 1 indexed citations

Patel, Chirag, et al.. (2021). Identification of antiviral phytochemicals as a potential SARS-CoV-2 main protease (Mpro) inhibitor using docking and molecular dynamics simulations. Scientific Reports. 11(1). 20295–20295. 34 indexed citations

10.

Patel, Chirag, et al.. (2021). Repurposing of anticancer phytochemicals for identifying potential fusion inhibitor for SARS-CoV-2 using molecular docking and molecular dynamics (MD) simulations. Journal of Biomolecular Structure and Dynamics. 40(17). 7744–7761. 16 indexed citations

11.

Kumar, Sivakumar Prasanth, et al.. (2021). Theaflavin-3-gallate, a natural antagonist for Hsp90: In-silico and in-vitro approach. Chemico-Biological Interactions. 353. 109774–109774. 8 indexed citations

12.

Patel, Chirag, et al.. (2020). Identification of potential inhibitors of coronavirus hemagglutinin-esterase using molecular docking, molecular dynamics simulation and binding free energy calculation. Molecular Diversity. 25(1). 421–433. 35 indexed citations

13.

Kumar, Sivakumar Prasanth, Chirag Patel, Prakash C. Jha, & Himanshu Pandya. (2017). Molecular dynamics-assisted pharmacophore modeling of caspase-3-isatin sulfonamide complex: Recognizing essential intermolecular contacts and features of sulfonamide inhibitor class for caspase-3 binding. Computational Biology and Chemistry. 71. 117–128. 15 indexed citations

14.

Lone, Mohsin Y., Sivakumar Prasanth Kumar, Mohd Athar, & Prakash C. Jha. (2017). Exploration of Mycobacterium tuberculosis structural proteome: An in-silico approach. Journal of Theoretical Biology. 439. 14–23. 12 indexed citations

15.

Kumar, Sivakumar Prasanth, Yogesh T. Jasrai, & Himanshu Pandya. (2016). Applications of Receptor- and Ligand-based Models in Inverse Docking Experiments: Recognition of Dihydrofolate Reductase Using 7,8-Dialkyl- 1,3-Diaminopyrrolo[3,2-f]Quinazolines. Current Computer - Aided Drug Design. 12(1). 15–28. 7 indexed citations

16.

Kumar, Sivakumar Prasanth & Prakash C. Jha. (2016). Multi-level structure-based pharmacophore modelling of caspase-3-non-peptide complexes: Extracting essential pharmacophore features and its application to virtual screening. Chemico-Biological Interactions. 254. 207–220. 11 indexed citations

17.

Kumar, Sivakumar Prasanth, Prakash C. Jha, Yogesh T. Jasrai, & Himanshu Pandya. (2015). The effect of various atomic partial charge schemes to elucidate consensus activity-correlating molecular regions: a test case of diverse QSAR models. Journal of Biomolecular Structure and Dynamics. 34(3). 540–559. 19 indexed citations

18.

Kumar, Sivakumar Prasanth, Prakash C. Jha, Himanshu Pandya, & Yogesh T. Jasrai. (2014). Implementation of pseudoreceptor-based pharmacophore queries in the prediction of probable protein targets: explorations in the protein structural profile of Zea mays. Molecular BioSystems. 10(7). 1833–1844. 7 indexed citations

19.

Kumar, Sivakumar Prasanth, et al.. (2014). A computational model for non-conserved mature miRNAs from the rice genome. SAR and QSAR in environmental research. 25(3). 205–220. 3 indexed citations

20.

Patel, Saumya, et al.. (2013). A Computational Approach towards the Understanding of Plasmodium falciparum Multidrug Resistance Protein 1. PubMed. 2013. 1–15. 7 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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