Madhu Chopra

1.1k total citations
57 papers, 865 citations indexed

About

Madhu Chopra is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Madhu Chopra has authored 57 papers receiving a total of 865 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Molecular Biology, 12 papers in Computational Theory and Mathematics and 11 papers in Organic Chemistry. Recurrent topics in Madhu Chopra's work include Computational Drug Discovery Methods (12 papers), Histone Deacetylase Inhibitors Research (7 papers) and Cancer therapeutics and mechanisms (6 papers). Madhu Chopra is often cited by papers focused on Computational Drug Discovery Methods (12 papers), Histone Deacetylase Inhibitors Research (7 papers) and Cancer therapeutics and mechanisms (6 papers). Madhu Chopra collaborates with scholars based in India, United States and Italy. Madhu Chopra's co-authors include Daman Saluja, Prakash Jha, Veena Agrawal, Parveen Kumar, Bilikere S. Dwarakanath, Gautam Behl, Uma Chaudhry, Vibha Pandey, Saurabh Dahiya and Monal Sharma and has published in prestigious journals such as Cancer Research, Scientific Reports and Journal of Colloid and Interface Science.

In The Last Decade

Madhu Chopra

54 papers receiving 840 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Madhu Chopra India 19 396 137 107 101 73 57 865
Isabella Romeo Italy 18 401 1.0× 191 1.4× 63 0.6× 94 0.9× 74 1.0× 52 891
Asif Shahriar Bangladesh 9 326 0.8× 81 0.6× 143 1.3× 92 0.9× 54 0.7× 20 766
Evelyn Winter Brazil 18 351 0.9× 233 1.7× 143 1.3× 49 0.5× 75 1.0× 35 826
Shahzaib Ahamad India 21 614 1.6× 163 1.2× 96 0.9× 209 2.1× 44 0.6× 47 1.1k
Roberto Parise‐Filho Brazil 17 315 0.8× 201 1.5× 51 0.5× 79 0.8× 74 1.0× 42 782
Mohd Shahbaaz South Africa 19 632 1.6× 133 1.0× 117 1.1× 203 2.0× 31 0.4× 56 1.2k
Tulika Bhardwaj India 20 651 1.6× 326 2.4× 145 1.4× 118 1.2× 96 1.3× 74 1.3k
Swati Jaiswal India 16 288 0.7× 146 1.1× 78 0.7× 43 0.4× 68 0.9× 47 857
Shui-Tein Chen Taiwan 18 625 1.6× 141 1.0× 146 1.4× 123 1.2× 61 0.8× 31 1.3k

Countries citing papers authored by Madhu Chopra

Since Specialization
Citations

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

Fields of papers citing papers by Madhu Chopra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Madhu Chopra

This figure shows the co-authorship network connecting the top 25 collaborators of Madhu Chopra. A scholar is included among the top collaborators of Madhu Chopra 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 Madhu Chopra. Madhu Chopra 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.
Kant, Ravi, Rahul Kaushik, Madhu Chopra, & Daman Saluja. (2024). Structure-based drug discovery to identify SARS-CoV2 spike protein–ACE2 interaction inhibitors. Journal of Biomolecular Structure and Dynamics. 43(7). 3652–3670. 2 indexed citations
2.
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Jha, Prakash, et al.. (2024). Immunoinformatic approach to design T cell epitope-based chimeric vaccine targeting multiple serotypes of dengue virus. Journal of Biomolecular Structure and Dynamics. 44(4). 1706–1724.
4.
Jha, Prakash & Madhu Chopra. (2023). COMBINATION OF LIGAND-BASED PHARMACOPHORE MODELLING, MOLECULAR DYNAMICS, AND DEEP LEARNING APPROACH TO IDENTIFY SELECTIVE PANK INHIBITORS AS ANTITUBERCULAR AGENTS.. International Journal of Infectious Diseases. 130. S6–S6. 1 indexed citations
5.
Jha, Prakash, et al.. (2023). Bactericidal activity of esculetin is associated with impaired cell wall synthesis by targeting glutamate racemase of Neisseria gonorrhoeae. Molecular Diversity. 28(5). 3181–3198. 2 indexed citations
6.
Kant, Ravi, Prakash Jha, Daman Saluja, & Madhu Chopra. (2022). Identification of novel inhibitors of Neisseria gonorrhoeae MurI using homology modeling, structure-based pharmacophore, molecular docking, and molecular dynamics simulation-based approach. Journal of Biomolecular Structure and Dynamics. 41(15). 7433–7446. 7 indexed citations
7.
Sharma, Monika, et al.. (2022). Pharmacophore-based virtual screening of ZINC database, molecular modeling and designing new derivatives as potential HDAC6 inhibitors. Molecular Diversity. 27(5). 2053–2071. 5 indexed citations
8.
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Verma, Priyanka, Krishna Dalal, & Madhu Chopra. (2016). Pharmacophore development and screening for discovery of potential inhibitors of ADAMTS-4 for osteoarthritis therapy. Journal of Molecular Modeling. 22(8). 178–178. 16 indexed citations
10.
Chopra, Madhu, et al.. (2016). Strong Anti-tumorous Potential of Nardostachys jatamansi Rhizome Extract on Glioblastoma and In Silico Analysis of its Molecular Drug Targets. Current Cancer Drug Targets. 17(1). 74–88. 17 indexed citations
11.
Behl, Gautam, et al.. (2013). PEG-coumarin based biocompatible self-assembled fluorescent nanoaggregates synthesized via click reactions and studies of aggregation behavior. Journal of Colloid and Interface Science. 416. 151–160. 17 indexed citations
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Mathur, Rohit, Nicolas Beaume, Anant Narayan Bhatt, et al.. (2010). Interaction and Structural Modification of Topoisomerase IIα by Peptidyl Prolyl Isomerase, pin1: An In Silico Study. Protein and Peptide Letters. 17(2). 151–163. 2 indexed citations
15.
Saluja, Daman, et al.. (2010). Inhibition of Human Cervical Cancer Cell Growth by Ethanolic Extract of Boerhaavia diffusa Linn. (Punarnava) Root. Evidence-based Complementary and Alternative Medicine. 2011(1). 427031–427031. 32 indexed citations
16.
Chopra, Madhu, et al.. (2009). Effect of varying chain length between P1 and P1′ position of tripeptidomimics on activity of angiotensin-converting enzyme inhibitors. Bioorganic & Medicinal Chemistry Letters. 19(15). 4364–4366. 32 indexed citations
17.
Dahiya, Saurabh, et al.. (2009). Synthesis and evaluation of Ciprofloxacin derivatives as diagnostic tools for bacterial infection by Staphylococcus aureus. Metallomics. 1(5). 409–409. 14 indexed citations
18.
Bansal, Seema, Prija Ponnan, Hanumantharao G. Raj, et al.. (2008). Autoacetylation of Purified Calreticulin Transacetylase Utilizing Acetoxycoumarin as the Acetyl Group Donor. Applied Biochemistry and Biotechnology. 152(1). 170–176. 1 indexed citations
19.
Chopra, Madhu, et al.. (2008). Molecular modeling study on chemically diverse series of cyclooxygenase-2 selective inhibitors: generation of predictive pharmacophore model using Catalyst. Journal of Molecular Modeling. 14(11). 1087–1099. 23 indexed citations
20.
Kumari, Saroj, et al.. (2004). Novel 99mTcradiolabeled quinazolinone derivative [Qn-In]: synthesis, evaluation and biodistribution studies in mice and rabbit. Nuclear Medicine and Biology. 31(8). 1087–1095. 9 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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