Sharad Verma

961 total citations
26 papers, 805 citations indexed

About

Sharad Verma is a scholar working on Molecular Biology, Oncology and Computational Theory and Mathematics. According to data from OpenAlex, Sharad Verma has authored 26 papers receiving a total of 805 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 8 papers in Oncology and 7 papers in Computational Theory and Mathematics. Recurrent topics in Sharad Verma's work include Computational Drug Discovery Methods (7 papers), Cancer-related Molecular Pathways (4 papers) and Cell death mechanisms and regulation (3 papers). Sharad Verma is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Cancer-related Molecular Pathways (4 papers) and Cell death mechanisms and regulation (3 papers). Sharad Verma collaborates with scholars based in India, United States and Germany. Sharad Verma's co-authors include Abha Mishra, Amit Singh, Abhinav Grover, Aditi Singh, Sukriti Goyal, Salma Jamal, Chetna Tyagi, Anil Kumar Tripathi, Soumitra Paul Chowdhury and R. Banerji and has published in prestigious journals such as PLoS ONE, Cancer Research and Gene.

In The Last Decade

Sharad Verma

26 papers receiving 779 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sharad Verma India 13 380 123 89 87 78 26 805
Florin Borcan Romania 17 340 0.9× 97 0.8× 57 0.6× 56 0.6× 51 0.7× 84 894
Evelyn Winter Brazil 18 351 0.9× 83 0.7× 63 0.7× 49 0.6× 52 0.7× 35 826
Ce Tang China 14 417 1.1× 187 1.5× 104 1.2× 33 0.4× 46 0.6× 45 996
Tamilselvam Rajavel India 10 421 1.1× 122 1.0× 106 1.2× 42 0.5× 27 0.3× 11 793
Nora M. Aborehab Egypt 16 281 0.7× 135 1.1× 86 1.0× 26 0.3× 28 0.4× 40 789
Iasmina Marcovici Romania 14 306 0.8× 102 0.8× 93 1.0× 49 0.6× 61 0.8× 34 763
Yusra Al Dhaheri United Arab Emirates 16 413 1.1× 180 1.5× 96 1.1× 34 0.4× 20 0.3× 27 865
Md. Moniruzzaman Bangladesh 19 246 0.6× 137 1.1× 35 0.4× 25 0.3× 79 1.0× 68 1.0k
Ahmed Abdrabou Egypt 22 379 1.0× 148 1.2× 38 0.4× 45 0.5× 114 1.5× 90 1.3k

Countries citing papers authored by Sharad Verma

Since Specialization
Citations

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

Fields of papers citing papers by Sharad Verma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sharad Verma

This figure shows the co-authorship network connecting the top 25 collaborators of Sharad Verma. A scholar is included among the top collaborators of Sharad Verma 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 Sharad Verma. Sharad Verma 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.
Whitehead, Christopher E., Scott Kopetz, Veera Baladandayuthapani, et al.. (2024). Abstract 1230: MTX-531, a first-in-class pan-PI3K inhibitor spares hyperinsulinemia yielding durable tumor regressions and resilience to adaptive resistance. Cancer Research. 84(6_Supplement). 1230–1230. 1 indexed citations
2.
Verma, Sharad, Sukriti Goyal, Anchala Kumari, et al.. (2018). Structural investigations on mechanism of lapatinib resistance caused by HER-2 mutants. PLoS ONE. 13(2). e0190942–e0190942. 7 indexed citations
3.
Verma, Sharad, Aditi Singh, Anchala Kumari, et al.. (2017). Dissecting the role of mutations in chymase inhibition: Free energy and decomposition analysis. Gene. 609. 68–79. 4 indexed citations
4.
Verma, Sharad, Sonam Grover, Chetna Tyagi, et al.. (2016). Hydrophobic Interactions Are a Key to MDM2 Inhibition by Polyphenols as Revealed by Molecular Dynamics Simulations and MM/PBSA Free Energy Calculations. PLoS ONE. 11(2). e0149014–e0149014. 77 indexed citations
5.
Verma, Sharad, Sukriti Goyal, Salma Jamal, Aditi Singh, & Abhinav Grover. (2016). Hsp90: Friends, clients and natural foes. Biochimie. 127. 227–240. 69 indexed citations
6.
Verma, Sharad, Sukriti Goyal, Chetna Tyagi, et al.. (2016). BIM (BCL-2 interacting mediator of cell death) SAHB (stabilized α helix of BCL2) not always convinces BAX (BCL-2-associated X protein) for apoptosis. Journal of Molecular Graphics and Modelling. 67. 94–101. 9 indexed citations
7.
Mishra, Abha, et al.. (2014). MMPs as Molecular Targets for Wound Healing by Musa sapientum: In-silico and In-vivo Evidences. 2(1). 39–46. 1 indexed citations
8.
Verma, Sharad, Amit Singh, & Abha Mishra. (2014). Complex disruption effect of natural polyphenols on Bcl-2-Bax: molecular dynamics simulation and essential dynamics study. Journal of Biomolecular Structure and Dynamics. 33(5). 1094–1106. 16 indexed citations
9.
Verma, Sharad, Amit Singh, & Abha Mishra. (2013). Gallic acid: Molecular rival of cancer. Environmental Toxicology and Pharmacology. 35(3). 473–485. 304 indexed citations
10.
Verma, Sharad, Amit Singh, & Abha Mishra. (2013). Molecular dynamics investigation on the poor sensitivity of A171T mutant NEDD8-activating enzyme (NAE) for MLN4924. Journal of Biomolecular Structure and Dynamics. 32(7). 1064–1073. 7 indexed citations
11.
Verma, Sharad, Amit Singh, & Abha Mishra. (2013). Molecular Dynamics Investigation on the Inhibition of MDM2‐p53 Interaction by Polyphenols. Molecular Informatics. 32(2). 203–212. 4 indexed citations
12.
Verma, Sharad, Amit Singh, & Abha Mishra. (2012). The effect of fulvic acid on pre‐ and postaggregation state of Aβ17–42: Molecular dynamics simulation studies. Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics. 1834(1). 24–33. 12 indexed citations
13.
Verma, Sharad, Amit Singh, & Abha Mishra. (2012). Dual inhibition of chaperoning process by taxifolin: Molecular dynamics simulation study. Journal of Molecular Graphics and Modelling. 37. 27–38. 24 indexed citations
14.
Verma, Sharad, Abha Mishra, & Amit Singh. (2012). Molecular construction of NADH-cytochrome b5 reductase inhibition by flavonoids and chemical basis of difference in inhibition potential: Molecular dynamics simulation study. 2 indexed citations
15.
16.
Verma, Sharad, Amit Singh, & Abha Mishra. (2012). Quercetin and taxifolin completely break MDM2–p53 association: molecular dynamics simulation study. Medicinal Chemistry Research. 22(6). 2778–2787. 3 indexed citations
17.
Verma, Sharad, Abha Mishra, N. S. Nagpure, et al.. (2012). Interaction between shrimp and white spot syndrome virus through PmRab7-VP28 complex: an insight using simulation and docking studies. Journal of Molecular Modeling. 19(3). 1285–1294. 24 indexed citations
18.
Verma, Sharad, Dhanapalan Nagarathnam, Lei Zhang, et al.. (2005). Substituted aminobenzimidazole pyrimidines as cyclin-dependent kinase inhibitors. Bioorganic & Medicinal Chemistry Letters. 15(8). 1973–1977. 18 indexed citations
19.
Chowdhury, Soumitra Paul, et al.. (2004). Molecular diversity of tannic acid degrading bacteria isolated from tannery soil. Journal of Applied Microbiology. 97(6). 1210–1219. 61 indexed citations
20.
Banerji, R., et al.. (1985). Jatropha seed oils for energy. Biomass. 8(4). 277–282. 70 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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