Dhanushya Gopal

585 total citations
9 papers, 522 citations indexed

About

Dhanushya Gopal is a scholar working on Organic Chemistry, Molecular Biology and Computational Theory and Mathematics. According to data from OpenAlex, Dhanushya Gopal has authored 9 papers receiving a total of 522 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Organic Chemistry, 5 papers in Molecular Biology and 3 papers in Computational Theory and Mathematics. Recurrent topics in Dhanushya Gopal's work include Click Chemistry and Applications (3 papers), Computational Drug Discovery Methods (3 papers) and Synthesis and biological activity (2 papers). Dhanushya Gopal is often cited by papers focused on Click Chemistry and Applications (3 papers), Computational Drug Discovery Methods (3 papers) and Synthesis and biological activity (2 papers). Dhanushya Gopal collaborates with scholars based in United States, India and Poland. Dhanushya Gopal's co-authors include Dhanapalan Nagarathnam, Mark Cushman, Chii M. Lin, Asit K. Chakraborti, Ernest Hamel, Mark Cushman, Chii M. Lin, Ernest Hamel, Robert L. Geahlen and Rex Devasahayam Arokia Balaya and has published in prestigious journals such as Journal of Medicinal Chemistry, Bioorganic & Medicinal Chemistry Letters and Journal of Biomolecular Structure and Dynamics.

In The Last Decade

Dhanushya Gopal

8 papers receiving 514 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dhanushya Gopal United States 6 401 206 88 69 47 9 522
Maria Fridén‐Saxin Sweden 10 343 0.9× 127 0.6× 142 1.6× 28 0.4× 61 1.3× 12 495
Giuseppina Grisolia Italy 10 296 0.7× 216 1.0× 51 0.6× 59 0.9× 112 2.4× 12 520
Domenico Alloatti Italy 8 225 0.6× 171 0.8× 49 0.6× 46 0.7× 7 0.1× 8 344
Sowjanya Polepalli India 17 554 1.4× 199 1.0× 77 0.9× 34 0.5× 6 0.1× 23 648
Jérémie Fournier Dit Chabert France 9 588 1.5× 187 0.9× 88 1.0× 39 0.6× 3 0.1× 10 677
Ali Nakhi India 13 282 0.7× 131 0.6× 35 0.4× 51 0.7× 104 2.2× 17 464
Wen Jian Tang China 9 273 0.7× 158 0.8× 70 0.8× 36 0.5× 4 0.1× 10 445
Rudraraju Ramesh Raju India 15 682 1.7× 179 0.9× 25 0.3× 36 0.5× 10 0.2× 33 720
Niefang Yu China 9 206 0.5× 142 0.7× 46 0.5× 31 0.4× 23 0.5× 13 346
Eman A. M. Beshr Egypt 18 529 1.3× 212 1.0× 92 1.0× 64 0.9× 4 0.1× 38 700

Countries citing papers authored by Dhanushya Gopal

Since Specialization
Citations

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

Fields of papers citing papers by Dhanushya Gopal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dhanushya Gopal

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

All Works

9 of 9 papers shown
1.
Gopal, Dhanushya, et al.. (2025). Integrated AI and machine learning pipeline identifies novel WEE1 kinase inhibitors for targeted cancer therapy. Molecular Diversity. 29(4). 3425–3448.
2.
Gopal, Dhanushya, et al.. (2024). TikTok and the fear of missing out: Analyzing social media consumption and mental wellbeing. 3(2). 1483–1483. 1 indexed citations
4.
Gopal, Dhanushya, et al.. (2024). Insightful t-SNE guided exploration spotlighting Palbociclib and Ribociclib analogues as novel WEE1 kinase inhibitory candidates. Journal of Biomolecular Structure and Dynamics. 43(9). 4716–4728. 12 indexed citations
5.
Cushman, Mark, et al.. (1992). Synthesis and evaluation of analogs of (Z)-1-(4-methoxyphenyl)-2-(3,4,5-trimethoxyphenyl)ethene as potential cytotoxic and antimitotic agents. Journal of Medicinal Chemistry. 35(12). 2293–2306. 215 indexed citations
6.
Cushman, Mark, Dhanapalan Nagarathnam, Dhanushya Gopal, et al.. (1991). Synthesis and evaluation of stilbene and dihydrostilbene derivatives as potential anticancer agents that inhibit tubulin polymerization. Journal of Medicinal Chemistry. 34(8). 2579–2588. 267 indexed citations
7.
Cushman, Mark, Dhanapalan Nagarathnam, Dhanushya Gopal, et al.. (1991). ChemInform Abstract: Synthesis and Evaluation of Stilbene and Dihydrostilbene Derivatives as Potential Anticancer Agents That Inhibit Tubulin Polymerization.. ChemInform. 22(52). 1 indexed citations
8.
Cushman, Mark, Dhanapalan Nagarathnam, Dhanushya Gopal, & Robert L. Geahlen. (1991). Synthesis and evaluation of new protein-tyrosine kinase inhibitors. Part 2. Phenylhydrazones.. Bioorganic & Medicinal Chemistry Letters. 1(4). 215–218. 12 indexed citations
9.
Cushman, Mark, Dhanapalan Nagarathnam, Dhanushya Gopal, & Robert L. Geahlen. (1991). Synthesis and evaluation of new protein-tyrosine kinase inhibitors. Part 1. pyridine-containing stilbenes and amides.. Bioorganic & Medicinal Chemistry Letters. 1(4). 211–214. 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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