Aravinda Pai

429 citations
55 papers · 309 indexed · h-index 9

Aravinda Pai

48 papers receiving 298 citations

Peers

Aravinda Pai
Comparison fields: 5 of 71
  • Drug Discovery 1
  • Computational Theory and Mathematics 82
  • Toxicology 16
  • Organic Chemistry 133
  • Pharmacology 50
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Citations per field
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Citations per year

Countries citing papers authored by Aravinda Pai

Since Specialization
Citations

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

Fields of papers citing papers by Aravinda Pai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Aravinda Pai, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Aravinda Pai Line = papers co-authored together Aravinda Pai links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20241
3 20248
4 20240
5 202319
6 20232
7 20221
8 20221
9 20221
10 20212
11
Investigation of Anti-Inflammatory potentials of terminalia tomentosa wight & Arn. Bark - An In-Vitro approach
20175
12
Pharmacophore based three dimensional structure activity relationship studies on a novel series of pyrimidine-7-ones as selective inhibitors of CDK4
20171
13
Evaluation of antimicrobial activity of Acalypha indica against various enteric gut pathogens
20171
14 20172
15
Structure activity relationship of Atropine analogues with muscarinic acetyl choline M1 receptor: A molecular docking based approach
20161
16 201615
17 201411
18
ROLE OF SHORT TERM CAMPAIGNS IN BOOSTING UP SALES, WITH SPECIAL EMPHASIS ON INDIAN PHARMACEUTICAL MARKET
20140
19 20133
20 20109

About Aravinda Pai

Aravinda Pai is a scholar working on Computational Theory and Mathematics, Organic Chemistry and Biochemistry, having authored 55 papers that have together received 309 indexed citations. Recurring topics across this work include Synthesis and biological activity (19 papers), Computational Drug Discovery Methods (14 papers), Synthesis and Characterization of Heterocyclic Compounds (6 papers), Advanced Breast Cancer Therapies (5 papers), Phytochemicals and Antioxidant Activities (4 papers), Multicomponent Synthesis of Heterocycles (4 papers), Enzyme Production and Characterization (4 papers) and Medicinal Plants and Neuroprotection (3 papers). The work is most often cited by research in Drug Discovery (1 citation), Computational Theory and Mathematics (82 citations) and Toxicology (16 citations). Aravinda Pai has collaborated with scholars based in India, Czechia and Malaysia. Frequent co-authors include B. S. Jayashree, Rupal Ojha, Vijay Kumar Prajapati, Banoth Karan Kumar, Faheem Faheem, Kondapalli Venkata Gowri Chandra Sekhar, Sankaranarayanan Murugesan, Rekha R. Shenoy, Richard Lobo and Sanchari Basu Mallik. Their work appears in journals such as SHILAP Revista de lepidopterología, RSC Advances and Arabian Journal of Chemistry.

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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