Krishna Bajjuri

1.2k total citations
12 papers, 456 citations indexed

About

Krishna Bajjuri is a scholar working on Molecular Biology, Organic Chemistry and Oncology. According to data from OpenAlex, Krishna Bajjuri has authored 12 papers receiving a total of 456 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 4 papers in Organic Chemistry and 4 papers in Oncology. Recurrent topics in Krishna Bajjuri's work include Receptor Mechanisms and Signaling (3 papers), Nicotinic Acetylcholine Receptors Study (3 papers) and Monoclonal and Polyclonal Antibodies Research (3 papers). Krishna Bajjuri is often cited by papers focused on Receptor Mechanisms and Signaling (3 papers), Nicotinic Acetylcholine Receptors Study (3 papers) and Monoclonal and Polyclonal Antibodies Research (3 papers). Krishna Bajjuri collaborates with scholars based in United States and United Kingdom. Krishna Bajjuri's co-authors include Subhash C. Sinha, Han Xiao, Seihyun Choi, Abhishek Chatterjee, Peter G. Schultz, Subhash C. Sinha, Cheng Liu, Yuan Liu, Yuan Liu and Alan P. Kozikowski and has published in prestigious journals such as Angewandte Chemie International Edition, Journal of Medicinal Chemistry and Bioconjugate Chemistry.

In The Last Decade

Krishna Bajjuri

11 papers receiving 447 citations

Peers

Krishna Bajjuri
Erika Orbán Hungary
J.S. Josan United States
Subhash C. Sinha United States
Anand S. Dutta United Kingdom
Edmund R. Marinelli United States
Krishna Bajjuri
Citations per year, relative to Krishna Bajjuri Krishna Bajjuri (= 1×) peers Donatella Diana

Countries citing papers authored by Krishna Bajjuri

Since Specialization
Citations

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

Fields of papers citing papers by Krishna Bajjuri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Krishna Bajjuri

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

All Works

12 of 12 papers shown
1.
Yuan, Robert, Andrew McGeehan, Sihong Zhou, et al.. (2025). The Anti-FRα Antibody–Drug Conjugate Luveltamab Tazevibulin Demonstrates Efficacy in Non–Small Cell Lung Cancer Preclinical Models and Induces Immunogenic Cell Death. Molecular Cancer Therapeutics. 24(9). 1428–1441. 1 indexed citations
3.
Zhu, Xiang‐Wei, et al.. (2023). Building Machine Learning Small Molecule Melting Points and Solubility Models Using CCDC Melting Points Dataset. Journal of Chemical Information and Modeling. 63(10). 2948–2959. 13 indexed citations
4.
Polyakov, Valery, et al.. (2022). Indexing Ultrafast Shape-Based Descriptors in MongoDB to Identify TLR4 Pathway Agonists. Journal of Chemical Information and Modeling. 62(10). 2446–2455. 2 indexed citations
5.
Xiao, Han, Abhishek Chatterjee, Seihyun Choi, et al.. (2013). Genetic Incorporation of Multiple Unnatural Amino Acids into Proteins in Mammalian Cells. Angewandte Chemie International Edition. 52(52). 14080–14083. 172 indexed citations
6.
Xiao, Han, Abhishek Chatterjee, Seihyun Choi, et al.. (2013). Genetic Incorporation of Multiple Unnatural Amino Acids into Proteins in Mammalian Cells. Angewandte Chemie. 125(52). 14330–14333. 43 indexed citations
7.
Liu, Yuan, Krishna Bajjuri, Cheng Liu, & Subhash C. Sinha. (2011). Targeting Cell Surface Alpha(v)beta(3) Integrin Increases Therapeutic Efficacies of a Legumain Protease-Activated Auristatin Prodrug. Molecular Pharmaceutics. 9(1). 168–175. 74 indexed citations
8.
Zhang, Hankun, Werner Tückmantel, J. Brek Eaton, et al.. (2011). Chemistry and Behavioral Studies Identify Chiral Cyclopropanes as Selective α4β2-Nicotinic Acetylcholine Receptor Partial Agonists Exhibiting an Antidepressant Profile. Journal of Medicinal Chemistry. 55(2). 717–724. 31 indexed citations
9.
Goswami, Rajib Kumar, Krishna Bajjuri, Jane Forsyth, et al.. (2011). Chemically Programmed Antibodies Targeting Multiple Alpha(v) Integrins and Their Effects on Tumor-Related Functions in Vitro. Bioconjugate Chemistry. 22(8). 1535–1544. 8 indexed citations
10.
Bajjuri, Krishna, et al.. (2010). The Legumain Protease‐Activated Auristatin Prodrugs Suppress Tumor Growth and Metastasis without Toxicity. ChemMedChem. 6(1). 54–59. 58 indexed citations
11.
Kozikowski, Alan P., J. Brek Eaton, Krishna Bajjuri, et al.. (2009). Chemistry and Pharmacology of Nicotinic Ligands Based on 6‐[5‐(Azetidin‐2‐ylmethoxy)pyridin‐3‐yl]hex‐5‐yn‐1‐ol (AMOP‐H‐OH) for Possible Use in Depression. ChemMedChem. 4(8). 1279–1291. 33 indexed citations
12.
Kozikowski, Alan P., et al.. (2007). Chemical Medicine: Novel 10‐Substituted Cytisine Derivatives with Increased Selectivity for α4β2 Nicotinic Acetylcholine Receptors. ChemMedChem. 2(8). 1157–1161. 21 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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