Nagasuma Chandra

5.9k total citations
181 papers, 4.2k citations indexed

About

Nagasuma Chandra is a scholar working on Molecular Biology, Infectious Diseases and Computational Theory and Mathematics. According to data from OpenAlex, Nagasuma Chandra has authored 181 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 149 papers in Molecular Biology, 57 papers in Infectious Diseases and 35 papers in Computational Theory and Mathematics. Recurrent topics in Nagasuma Chandra's work include Tuberculosis Research and Epidemiology (50 papers), Computational Drug Discovery Methods (35 papers) and RNA and protein synthesis mechanisms (28 papers). Nagasuma Chandra is often cited by papers focused on Tuberculosis Research and Epidemiology (50 papers), Computational Drug Discovery Methods (35 papers) and RNA and protein synthesis mechanisms (28 papers). Nagasuma Chandra collaborates with scholars based in India, United States and United Kingdom. Nagasuma Chandra's co-authors include Karthik Raman, Kalidas Yeturu, M. Vijayan, Jyothi Padiadpu, Ramachandraiah Gosu, P. T. Ravi Rajagopalan, Deepesh Nagarajan, Narayanaswamy Srinivasan, K. Muniyappa and Sankaran Sandhya and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and SHILAP Revista de lepidopterología.

In The Last Decade

Nagasuma Chandra

179 papers receiving 4.1k citations

Peers

Nagasuma Chandra

EPIDE

CTM

IMMUN

Markus Wiederstein Austria

Dmitri Beglov United States

Diógenes Santiago Santos Brazil

József Tőzsér Hungary

M.M. Cherney Canada

Juergen Haas Germany

Matheus Froeyen Belgium

João Rodrigues Netherlands

Hideki Aihara United States

David L. Pompliano United States

Markus Wiederstein Austria

Nagasuma Chandra

3.6k ×1.2

614 ×0.6

458 ×0.7

EPIDE

499 ×0.8

CTM

598 ×1.4

IMMUN

Citations per year, relative to Nagasuma Chandra Nagasuma Chandra (= 1×) peers Markus Wiederstein

Countries citing papers authored by Nagasuma Chandra

Since Specialization

Citations

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

Fields of papers citing papers by Nagasuma Chandra

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nagasuma Chandra

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

All Works

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20 of 20 papers shown

Chandra, Nagasuma, et al.. (2024). CRD: A de novo design algorithm for the prediction of cognate protein receptors for small molecule ligands. Structure. 32(3). 362–375.e4. 2 indexed citations

Singh, Indira, et al.. (2023). Regenerative agriculture augments bacterial community structure for a healthier soil and agriculture. Frontiers in Agronomy. 5. 1134514–1134514. 12 indexed citations

Kohli, Sakshi, Raju S Rajmani, Nagasuma Chandra, et al.. (2023). Cysteine desulfurase (IscS)–mediated fine-tuning of bioenergetics and SUF expression prevents Mycobacterium tuberculosis hypervirulence. Science Advances. 9(50). eadh2858–eadh2858. 7 indexed citations

Singh, Amit, et al.. (2022). A 9-gene biomarker panel identifies bacterial coinfections in culture-negative COVID-19 cases. Molecular Omics. 18(8). 814–820. 10 indexed citations

Singh, Samsher, Chandrani Thakur, Sakshi Kohli, et al.. (2022). Moxifloxacin-Mediated Killing of Mycobacterium tuberculosis Involves Respiratory Downshift, Reductive Stress, and Accumulation of Reactive Oxygen Species. Antimicrobial Agents and Chemotherapy. 66(9). e0059222–e0059222. 29 indexed citations

Anand, Kushi, Chandrani Thakur, Raju S Rajmani, et al.. (2022). Mycobacterium tuberculosis requires SufT for Fe-S cluster maturation, metabolism, and survival in vivo. PLoS Pathogens. 18(4). e1010475–e1010475. 17 indexed citations

Nagarajan, Deepesh, et al.. (2019). Development and Characterization of a Potent Tumor Necrosis Factor-Alpha-Blocking Agent. Monoclonal Antibodies in Immunodiagnosis and Immunotherapy. 38(4). 145–156. 1 indexed citations

Mishra, Saurabh, Prashant Shukla, Ashima Bhaskar, et al.. (2017). Efficacy of β-lactam/β-lactamase inhibitor combination is linked to WhiB4-mediated changes in redox physiology of Mycobacterium tuberculosis. eLife. 6. 46 indexed citations

Chandra, Nagasuma, et al.. (2015). Recognizing drug targets using evolutionary information: implications for repurposing FDA-approved drugs against Mycobacterium tuberculosis H37Rv. Molecular BioSystems. 11(12). 3316–3331. 14 indexed citations

10.

Ghosh, Soma, Nagasuma Chandra, & Saraswathi Vishveshwara. (2015). Mechanism of Iron-Dependent Repressor (IdeR) Activation and DNA Binding: A Molecular Dynamics and Protein Structure Network Study. PLoS Computational Biology. 11(12). e1004500–e1004500. 19 indexed citations

11.

Mukherjee, Sumanta, Jim Warwicker, & Nagasuma Chandra. (2015). Deciphering complex patterns of class‐I HLA–peptide cross‐reactivity via hierarchical grouping. Immunology and Cell Biology. 93(6). 522–532. 7 indexed citations

12.

Raghunath, Arathi, et al.. (2015). A molecular systems approach to modelling human skin pigmentation: identifying underlying pathways and critical components. BMC Research Notes. 8(1). 170–170. 10 indexed citations

13.

Ghosh, Soma, Krishnamachar Prasad, Saraswathi Vishveshwara, & Nagasuma Chandra. (2011). Rule-based modelling of iron homeostasis in tuberculosis. Molecular BioSystems. 7(10). 2750–2768. 8 indexed citations

14.

Raman, Karthik, et al.. (2009). A systems perspective of host–pathogen interactions: predicting disease outcome in tuberculosis. Molecular BioSystems. 6(3). 516–530. 34 indexed citations

15.

Raman, Karthik, Rohit Vashisht, & Nagasuma Chandra. (2009). Strategies for efficient disruption of metabolism in Mycobacterium tuberculosis from network analysis. Molecular BioSystems. 5(12). 1740–1751. 25 indexed citations

16.

Shaila, M.S., et al.. (2008). HLA‐A*0201‐restricted Cytotoxic T‐cell Epitopes in Three PE/PPE Family Proteins of Mycobacterium tuberculosis. Scandinavian Journal of Immunology. 67(4). 411–417. 14 indexed citations

17.

Raman, Karthik & Nagasuma Chandra. (2008). Mycobacterium tuberculosis interactome analysis unravels potential pathways to drug resistance. BMC Microbiology. 8(1). 234–234. 77 indexed citations

18.

Jeyakani, Justin, et al.. (2007). CancerLectinDB: a database of lectins relevant to cancer. Glycoconjugate Journal. 25(3). 191–198. 38 indexed citations

19.

Chandra, Nagasuma, et al.. (2006). Carbohydrate-based drug design: Recognition fingerprints and their use in lead identification. INDIAN JOURNAL OF CHEMISTRY- SECTION A. 45(1). 77–92. 2 indexed citations

20.

Muniyappa, K., N. Ganesh, Pawan Singh, et al.. (2004). Homologous recombination in mycobacteria. Current Science. 86(1). 141–148. 2 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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