Deepti Goyal

1.1k total citations
21 papers, 898 citations indexed

About

Deepti Goyal is a scholar working on Physiology, Pharmacology and Computational Theory and Mathematics. According to data from OpenAlex, Deepti Goyal has authored 21 papers receiving a total of 898 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Physiology, 9 papers in Pharmacology and 9 papers in Computational Theory and Mathematics. Recurrent topics in Deepti Goyal's work include Alzheimer's disease research and treatments (14 papers), Computational Drug Discovery Methods (9 papers) and Cholinesterase and Neurodegenerative Diseases (8 papers). Deepti Goyal is often cited by papers focused on Alzheimer's disease research and treatments (14 papers), Computational Drug Discovery Methods (9 papers) and Cholinesterase and Neurodegenerative Diseases (8 papers). Deepti Goyal collaborates with scholars based in India, Netherlands and United States. Deepti Goyal's co-authors include Bhupesh Goyal, Suniba Shuaib, Amandeep Kaur, Nitesh Priyadarshi, Nitin Kumar Singhal and Prit Pal Singh and has published in prestigious journals such as The Journal of Physical Chemistry B, Physical Chemistry Chemical Physics and Journal of Cellular Biochemistry.

In The Last Decade

Deepti Goyal

20 papers receiving 889 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Deepti Goyal India 14 397 369 334 208 186 21 898
Bhupesh Goyal India 20 604 1.5× 468 1.3× 484 1.4× 245 1.2× 232 1.2× 65 1.3k
Sean A. Hudson United Kingdom 9 661 1.7× 205 0.6× 412 1.2× 179 0.9× 163 0.9× 10 1.2k
Olujide O. Olubiyi Nigeria 18 383 1.0× 169 0.5× 170 0.5× 173 0.8× 31 0.2× 40 796
Federica Prati Italy 17 308 0.8× 306 0.8× 220 0.7× 276 1.3× 390 2.1× 24 939
Sunhye Hong South Korea 10 355 0.9× 279 0.8× 102 0.3× 126 0.6× 75 0.4× 13 836
Sebastián A. Andújar Argentina 19 410 1.0× 192 0.5× 70 0.2× 314 1.5× 149 0.8× 42 816
Antonio Carrieri Italy 25 827 2.1× 284 0.8× 96 0.3× 744 3.6× 354 1.9× 99 1.8k
Samir Yahiaoui France 22 472 1.2× 182 0.5× 81 0.2× 435 2.1× 398 2.1× 40 1.1k
Eduarda Mendes Portugal 17 328 0.8× 164 0.4× 112 0.3× 336 1.6× 262 1.4× 38 900
Michele Tonelli Italy 24 542 1.4× 243 0.7× 79 0.2× 750 3.6× 210 1.1× 62 1.6k

Countries citing papers authored by Deepti Goyal

Since Specialization
Citations

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

Fields of papers citing papers by Deepti Goyal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Deepti Goyal

This figure shows the co-authorship network connecting the top 25 collaborators of Deepti Goyal. A scholar is included among the top collaborators of Deepti 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 Deepti Goyal. Deepti 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, 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.
2.
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
3.
4.
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
5.
Goyal, Deepti, et al.. (2023). Unveiling How Hydroxytyrosol Destabilizes α-Syn Oligomers Using Molecular Simulations. The Journal of Physical Chemistry B. 127(25). 5620–5632. 9 indexed citations
6.
Goyal, Deepti, et al.. (2023). Unravelling the destabilization potential of ellagic acid on α-synuclein fibrils using molecular dynamics simulations. Physical Chemistry Chemical Physics. 25(11). 8128–8143. 7 indexed citations
7.
Kaur, Amandeep, et al.. (2023). Triazole–Peptide Conjugate as a Modulator of Aβ-Aggregation, Metal-Mediated Aβ-Aggregation, and Cytotoxicity. ACS Chemical Neuroscience. 14(9). 1631–1645. 18 indexed citations
8.
Goyal, Deepti, et al.. (2022). Mechanistic insights into the mitigation of Aβ aggregation and protofibril destabilization by ad-enantiomeric decapeptide rk10. Physical Chemistry Chemical Physics. 24(36). 21975–21994. 19 indexed citations
9.
10.
Goyal, Deepti, et al.. (2020). Targeting Human Islet Amyloid Polypeptide Aggregation and Toxicity in Type 2 Diabetes: An Overview of Peptide-Based Inhibitors. Chemical Research in Toxicology. 33(11). 2719–2738. 24 indexed citations
11.
Goyal, Bhupesh & Deepti Goyal. (2020). Targeting the Dimerization of the Main Protease of Coronaviruses: A Potential Broad-Spectrum Therapeutic Strategy. ACS Combinatorial Science. 22(6). 297–305. 252 indexed citations
12.
Goyal, Deepti, et al.. (2020). An α-helix mimetic oligopyridylamide, ADH-31, modulates Aβ42 monomer aggregation and destabilizes protofibril structures: insights from molecular dynamics simulations. Physical Chemistry Chemical Physics. 22(48). 28055–28073. 19 indexed citations
13.
14.
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
15.
Kaur, Amandeep, et al.. (2019). Multifunctional Mono-Triazole Derivatives Inhibit Aβ42 Aggregation and Cu2+-Mediated Aβ42 Aggregation and Protect Against Aβ42-Induced Cytotoxicity. Chemical Research in Toxicology. 32(9). 1824–1839. 31 indexed citations
16.
Shuaib, Suniba, et al.. (2019). Computational design and evaluation of β‐sheet breaker peptides for destabilizing Alzheimer's amyloid‐β42 protofibrils. Journal of Cellular Biochemistry. 120(10). 17935–17950. 20 indexed citations
17.
Goyal, Deepti, et al.. (2019). Inhibition of Alzheimer’s amyloid-β42 peptide aggregation by a bi-functional bis-tryptoline triazole: key insights from molecular dynamics simulations. Journal of Biomolecular Structure and Dynamics. 38(6). 1–14. 19 indexed citations
18.
Goyal, Deepti, Amandeep Kaur, & Bhupesh Goyal. (2018). Benzofuran and Indole: Promising Scaffolds for Drug Development in Alzheimer's Disease. ChemMedChem. 13(13). 1275–1299. 135 indexed citations
19.
Shuaib, Suniba, et al.. (2018). Insights into the inhibitory mechanism of a resveratrol and clioquinol hybrid against Aβ42 aggregation and protofibril destabilization: A molecular dynamics simulation study. Journal of Biomolecular Structure and Dynamics. 37(12). 3183–3197. 39 indexed citations
20.
Goyal, Deepti, et al.. (2017). Rationally Designed Peptides and Peptidomimetics as Inhibitors of Amyloid-β (Aβ) Aggregation: Potential Therapeutics of Alzheimer’s Disease. ACS Combinatorial Science. 19(2). 55–80. 187 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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