Bhupesh Goyal

1.7k total citations
65 papers, 1.3k citations indexed

About

Bhupesh Goyal is a scholar working on Molecular Biology, Physiology and Computational Theory and Mathematics. According to data from OpenAlex, Bhupesh Goyal has authored 65 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 36 papers in Physiology and 25 papers in Computational Theory and Mathematics. Recurrent topics in Bhupesh Goyal's work include Alzheimer's disease research and treatments (36 papers), Protein Structure and Dynamics (25 papers) and Computational Drug Discovery Methods (25 papers). Bhupesh Goyal is often cited by papers focused on Alzheimer's disease research and treatments (36 papers), Protein Structure and Dynamics (25 papers) and Computational Drug Discovery Methods (25 papers). Bhupesh Goyal collaborates with scholars based in India, United States and Canada. Bhupesh Goyal's co-authors include Deepti Goyal, Suniba Shuaib, Amandeep Kaur, Susheel Durani, Deepti Goyal, Kinshuk Raj Srivastava, Nitesh Priyadarshi, Nitin Kumar Singhal, Anil Kumar and Bhupender Pal and has published in prestigious journals such as The Journal of Physical Chemistry B, Nanoscale and Physical Chemistry Chemical Physics.

In The Last Decade

Bhupesh Goyal

57 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Bhupesh Goyal India 20 604 484 468 245 232 65 1.3k
Deepti Goyal India 14 397 0.7× 334 0.7× 369 0.8× 208 0.8× 186 0.8× 21 898
Sean A. Hudson United Kingdom 9 661 1.1× 412 0.9× 205 0.4× 179 0.7× 163 0.7× 10 1.2k
Tuomo Laitinen Finland 26 970 1.6× 121 0.3× 193 0.4× 354 1.4× 278 1.2× 90 1.8k
Kunqian Yu China 23 844 1.4× 49 0.1× 326 0.7× 211 0.9× 95 0.4× 56 1.4k
Antonio Carrieri Italy 25 827 1.4× 96 0.2× 284 0.6× 744 3.0× 354 1.5× 99 1.8k
Olujide O. Olubiyi Nigeria 18 383 0.6× 170 0.4× 169 0.4× 173 0.7× 31 0.1× 40 796
Satyendra Mishra India 17 426 0.7× 159 0.3× 69 0.1× 338 1.4× 140 0.6× 56 1.1k
Benjamin J. Orlando United States 15 801 1.3× 65 0.1× 144 0.3× 322 1.3× 322 1.4× 24 1.6k
Farah Anjum Saudi Arabia 18 594 1.0× 95 0.2× 180 0.4× 119 0.5× 104 0.4× 72 1.0k

Countries citing papers authored by Bhupesh Goyal

Since Specialization
Citations

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

Fields of papers citing papers by Bhupesh Goyal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Bhupesh Goyal

This figure shows the co-authorship network connecting the top 25 collaborators of Bhupesh Goyal. A scholar is included among the top collaborators of Bhupesh Goyal 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 Bhupesh Goyal. Bhupesh Goyal 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.
Kaur, Gurmeet & Bhupesh Goyal. (2025). Ligand-based virtual screening to discover potential inhibitors of SARS-CoV-2 main protease. Physical Chemistry Chemical Physics. 27(37). 19877–19897.
2.
Goyal, Bhupesh, et al.. (2025). Inhibitory mechanism of lithospermic acid on the fibrillation of type 2 diabetes associated islet amyloid polypeptide. Journal of Molecular Graphics and Modelling. 136. 108972–108972. 1 indexed citations
3.
Kaur, Amandeep, Nitesh Priyadarshi, Prit Pal Singh, et al.. (2025). The Synergistic Potential of Rationally Designed Phenol-Triazole Derivatives to Attenuate Aβ/Cu2+–Aβ Aggregation and Reactive Oxygen Species. ACS Chemical Neuroscience. 16(15). 3020–3037.
4.
Goyal, Bhupesh, et al.. (2024). Deciphering the impact of F23L mutation on the aggregation propensity of human islet amyloid polypeptide using molecular simulations. Journal of Molecular Liquids. 411. 125775–125775. 1 indexed citations
5.
Goyal, Deepti, et al.. (2024). Insights into the baicalein-induced destabilization of LS-shaped Aβ42 protofibrils using computer simulations. Physical Chemistry Chemical Physics. 26(23). 16674–16686. 1 indexed citations
6.
7.
Priyadarshi, Nitesh, et al.. (2024). Exploring the Impact of C‐Terminal Based Pentapeptides on the Disassembly of Aβ42 Fibrils. ChemMedChem. 19(22). e202400486–e202400486. 1 indexed citations
8.
Goyal, Bhupesh, et al.. (2024). Delineating the impact of N21D mutation on the conformational preferences and structural transitions in human islet amyloid polypeptide. Journal of Molecular Liquids. 401. 124528–124528. 4 indexed citations
9.
10.
Garg, Mansi, et al.. (2024). Factor defining the effects of tetraalkylammonium chloride on stability, folding, and dynamics of horse cytochrome c. International Journal of Biological Macromolecules. 276(Pt 1). 133713–133713.
11.
Gupta, Ritika, Vishal Singh, Nitesh Priyadarshi, et al.. (2023). Salmonella typhimurium detection and ablation using OmpD specific aptamer with non-magnetic and magnetic graphene oxide. Biosensors and Bioelectronics. 234. 115354–115354. 26 indexed citations
12.
Kaur, Gurmeet & Bhupesh Goyal. (2023). Insights into the Interaction Mechanism of Boceprevir with SARS‐CoV‐2 Main Protease. ChemistrySelect. 8(28). 1 indexed citations
13.
Goyal, Bhupesh, et al.. (2023). Identification of new pentapeptides as potential inhibitors of amyloid–β42 aggregation using virtual screening and molecular dynamics simulations. Journal of Molecular Graphics and Modelling. 124. 108558–108558. 7 indexed citations
14.
Singh, Pritpal, et al.. (2023). Structural and molecular insights into tacrine-benzofuran hybrid induced inhibition of amyloid-β peptide aggregation and BACE1 activity. Journal of Biomolecular Structure and Dynamics. 41(22). 13211–13227.
15.
16.
Goyal, Bhupesh, et al.. (2020). Effect of Piedmont mutation (L34V) on the structure, dynamics, and aggregation of Alzheimer’s Aβ40 peptide. Journal of Molecular Graphics and Modelling. 97. 107571–107571. 8 indexed citations
17.
Garg, Mansi, et al.. (2020). Effect of imidazolium based ionic liquids on CO-association dynamics and thermodynamic stability of Ferrocytochrome c. Biophysical Chemistry. 268. 106497–106497. 3 indexed citations
18.
Kaur, Amandeep, et al.. (2019). Multi-target-directed triazole derivatives as promising agents for the treatment of Alzheimer’s disease. Bioorganic Chemistry. 87. 572–584. 58 indexed citations
19.
Shuaib, Suniba & Bhupesh Goyal. (2017). Scrutiny of the mechanism of small molecule inhibitor preventing conformational transition of amyloid-β 42 monomer: insights from molecular dynamics simulations. Journal of Biomolecular Structure and Dynamics. 36(3). 663–678. 34 indexed citations
20.
Shuaib, Suniba, et al.. (2017). Molecular insights into the inhibitory mechanism of rifamycin SV against β2–microglobulin aggregation: A molecular dynamics simulation study. International Journal of Biological Macromolecules. 102. 1025–1034. 20 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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