Kohulan Rajan

2.0k citations
29 papers · 681 indexed · 1 hit paper · h-index 11
Topics
Computational Drug Discovery Methods (15 papers)Machine Learning in Materials Science (11 papers)Metabolomics and Mass Spectrometry Studies (7 papers)

In The Last Decade

Kohulan Rajan

26 papers receiving 660 citations

Hit Papers

COCONUT online: Collection of Open Natural Products database20212026202220242021100200300

Peers

Kohulan Rajan
Comparison fields: 5 of 117
  • Molecular Biology 347
  • Computational Theory and Mathematics 314
  • Materials Chemistry 144
  • Pharmacology 112
  • Spectroscopy 62
Replace Arvid Berg with:
Arvid Berg Sweden
John W. Mayfield United States
Stefano Rensi United States
Gilleain Torrance United Kingdom
Christos A. Nicolaou United States
Xiaohua Zhang United States
Edgar Luttmann Germany
Jonathan Alvarsson Sweden
Eloy Félix United Kingdom
Oliver Horlacher Switzerland
Kohulan Rajan relative to Arvid Berg Sweden Arvid Berg's profile →
Citations per field
00.5×4.0×
Arvid Berg · 1×
Citations per year

Countries citing papers authored by Kohulan Rajan

Since Specialization
Citations

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

Fields of papers citing papers by Kohulan Rajan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kohulan Rajan

This figure shows the co-authorship network connecting the top 25 collaborators of Kohulan Rajan. A scholar is included among the top collaborators of Kohulan Rajan 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 Kohulan Rajan. Kohulan Rajan 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
#WorkIndexed citations
1 0
2 1
3 1
4 27
5 14
6 6
7 34
8 10
9 8
10 34
11 1
12 38
13
COCONUT online: Collection of Open Natural Products databasebreakdown →
346
14 15
15 49
16 1
17 43
18
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0
19 1
20 10

About Kohulan Rajan

Kohulan Rajan is a scholar working on Computational Theory and Mathematics, Computer Vision and Pattern Recognition and Hardware and Architecture, having authored 29 papers that have together received 681 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (15 papers), Machine Learning in Materials Science (11 papers) and Metabolomics and Mass Spectrometry Studies (7 papers). The work is most often cited by research in Computational Theory and Mathematics (314 citations), Pharmacology (112 citations) and Molecular Biology (347 citations). Kohulan Rajan has collaborated with scholars based in Germany, India and United States. Frequent co-authors include Christoph Steinbeck, Achim Zielesny, Maria Sorokina, Peter Merseburger, Mehmet Aziz Yirik, Venkata Chandrasekhar Nainala, Fahimuddin Shaik, Balasubramaniam Natarajan, Nisha Sharma and R. Váni. Their work appears in journals such as Nucleic Acids Research, Nature Communications and Current Opinion in Structural Biology.

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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