Neeraj Kumar

3.3k total citations
55 papers, 1.6k citations indexed

About

Neeraj Kumar is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Neeraj Kumar has authored 55 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Molecular Biology, 17 papers in Materials Chemistry and 13 papers in Computational Theory and Mathematics. Recurrent topics in Neeraj Kumar's work include Computational Drug Discovery Methods (13 papers), Machine Learning in Materials Science (11 papers) and Protein Structure and Dynamics (9 papers). Neeraj Kumar is often cited by papers focused on Computational Drug Discovery Methods (13 papers), Machine Learning in Materials Science (11 papers) and Protein Structure and Dynamics (9 papers). Neeraj Kumar collaborates with scholars based in United States, India and Poland. Neeraj Kumar's co-authors include Thomas Clavel, Simone Raugei, Ilias Lagkouvardos, Sandra E. Fischer, Rajendra P. Joshi, Pawel M. Kozlowski, Michael L. Pegis, James M. Mayer, R. Morris Bullock and Bradley A. McKeown and has published in prestigious journals such as Journal of the American Chemical Society, Angewandte Chemie International Edition and PLoS ONE.

In The Last Decade

Neeraj Kumar

51 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Neeraj Kumar United States 22 531 462 401 296 200 55 1.6k
Juan Xia China 22 398 0.7× 407 0.9× 597 1.5× 548 1.9× 62 0.3× 87 1.9k
John Cort United States 32 1.5k 2.9× 186 0.4× 447 1.1× 111 0.4× 54 0.3× 98 3.1k
Pin Chen China 26 244 0.5× 349 0.8× 704 1.8× 229 0.8× 308 1.5× 91 1.9k
Huanhuan Li China 27 1.3k 2.4× 44 0.1× 646 1.6× 251 0.8× 84 0.4× 119 2.3k
Yiming Mo China 21 393 0.7× 211 0.5× 447 1.1× 141 0.5× 97 0.5× 53 1.6k
Yongxi Zhao China 37 2.8k 5.3× 341 0.7× 1.0k 2.5× 521 1.8× 152 0.8× 100 4.7k
Yali Cui China 25 939 1.8× 144 0.3× 489 1.2× 184 0.6× 29 0.1× 141 2.3k
Changhao Wang China 20 487 0.9× 876 1.9× 894 2.2× 1.0k 3.5× 54 0.3× 88 2.5k
Pragya Singh United States 23 1.1k 2.1× 129 0.3× 733 1.8× 729 2.5× 55 0.3× 51 3.5k
Huaihong Cai China 31 966 1.8× 76 0.2× 741 1.8× 241 0.8× 40 0.2× 78 2.1k

Countries citing papers authored by Neeraj Kumar

Since Specialization
Citations

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

Fields of papers citing papers by Neeraj Kumar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Neeraj Kumar

This figure shows the co-authorship network connecting the top 25 collaborators of Neeraj Kumar. A scholar is included among the top collaborators of Neeraj Kumar 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 Neeraj Kumar. Neeraj Kumar 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.
Anderson, Lindsey, Charles Tapley Hoyt, Jeremy Zucker, et al.. (2025). Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions. Frontiers in Immunology. 16. 1502484–1502484. 7 indexed citations
2.
Zhang, Peiying, et al.. (2024). Generative adversarial imitation learning assisted virtual network embedding algorithm for space-air-ground integrated network. Computer Communications. 228. 107936–107936. 2 indexed citations
3.
Wilson, Jesse, Natalia Maltseva, Katherine Schultz, et al.. (2024). Native Mass Spectrometry Dissects the Structural Dynamics of an Allosteric Heterodimer of SARS-CoV-2 Nonstructural Proteins. Journal of the American Society for Mass Spectrometry. 35(5). 912–921. 4 indexed citations
4.
Anderson, Lindsey, Yasuhiro Oda, William Nelson, et al.. (2024). Profiling sorghum-microbe interactions with a specialized photoaffinity probe identifies key sorgoleone binders in Acinetobacter pittii. Applied and Environmental Microbiology. 90(10). e0102624–e0102624. 1 indexed citations
5.
Meena, Mohan Lal, Neeraj Kumar, J. Nanjundan, et al.. (2024). Breeding Brassica juncea hybrids with higher seed weight and oil content: Defining criteria for selection of parents. Heliyon. 10(23). e40555–e40555. 1 indexed citations
6.
Kim, Doo Nam, et al.. (2024). Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions. Bioengineering. 11(2). 185–185. 19 indexed citations
7.
Kumar, Kamal, et al.. (2023). GenSMILES: An enhanced validity conscious representation for inverse design of molecules. Knowledge-Based Systems. 268. 110429–110429. 10 indexed citations
8.
Kumar, Kamal, et al.. (2023). NRC-VABS: Normalized Reparameterized Conditional Variational Autoencoder with applied beam search in latent space for drug molecule design. Expert Systems with Applications. 240. 122396–122396. 5 indexed citations
9.
Kumar, Kamal, et al.. (2023). GMG-NCDVAE: Guided de novo Molecule Generation using NLP Techniques and Constrained Diverse Variational Autoencoder. ACM Transactions on Asian and Low-Resource Language Information Processing. 3 indexed citations
10.
11.
Kim, Ho Shin, et al.. (2022). Mechanistic investigation of SARS-CoV-2 main protease to accelerate design of covalent inhibitors. Scientific Reports. 12(1). 21037–21037. 5 indexed citations
12.
Kumar, Neeraj, et al.. (2022). Transcriptome analysis reveals cell cycle-related transcripts as key determinants of varietal differences in seed size of Brassica juncea. Scientific Reports. 12(1). 11713–11713. 5 indexed citations
13.
Bredeweg, Erin, Jeremy Zucker, Nathalie Munoz Munoz, et al.. (2021). Bayesian Inference for Integrating Yarrowia lipolytica Multiomics Datasets with Metabolic Modeling. ACS Synthetic Biology. 10(11). 2968–2981. 8 indexed citations
14.
Joshi, Rajendra P., et al.. (2021). Quantum Mechanical Methods Predict Accurate Thermodynamics of Biochemical Reactions. ACS Omega. 6(14). 9948–9959. 17 indexed citations
15.
Pegis, Michael L., Daniel J. Martin, Catherine F. Wise, et al.. (2019). Mechanism of Catalytic O2 Reduction by Iron Tetraphenylporphyrin. Journal of the American Chemical Society. 141(20). 8315–8326. 132 indexed citations
16.
Hurley, Jennifer, T. M. Finch, Jeremy Zucker, et al.. (2018). Circadian Proteomic Analysis Uncovers Mechanisms of Post-Transcriptional Regulation in Metabolic Pathways. Cell Systems. 7(6). 613–626.e5. 74 indexed citations
17.
Lagkouvardos, Ilias, Sandra E. Fischer, Neeraj Kumar, & Thomas Clavel. (2017). Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons. PeerJ. 5. e2836–e2836. 267 indexed citations
18.
Dobhal, Rajendra, et al.. (2014). Technological Empowerment of Women and Scientific Paper Writing. Current Science. 107(7). 1097–1098. 2 indexed citations
19.
Kumar, Neeraj, Piotr Lodowski, Maria Jaworska, et al.. (2013). Electronic structure of the S1 state in methylcobalamin: Insight from CASSCF/MC‐XQDPT2, EOM‐CCSD, and TD‐DFT calculations. Journal of Computational Chemistry. 34(12). 987–1004. 48 indexed citations
20.
Kumar, Neeraj, et al.. (2010). A REVIEW ON ANGIOGENESIS ASSAYS. International Journal of Pharmaceutical Sciences and Drug Research. 159–164.

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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