Prabha Garg

2.2k total citations
111 papers, 1.6k citations indexed

About

Prabha Garg is a scholar working on Molecular Biology, Computational Theory and Mathematics and Infectious Diseases. According to data from OpenAlex, Prabha Garg has authored 111 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Molecular Biology, 40 papers in Computational Theory and Mathematics and 15 papers in Infectious Diseases. Recurrent topics in Prabha Garg's work include Computational Drug Discovery Methods (40 papers), Biochemical and Molecular Research (11 papers) and Cholinesterase and Neurodegenerative Diseases (10 papers). Prabha Garg is often cited by papers focused on Computational Drug Discovery Methods (40 papers), Biochemical and Molecular Research (11 papers) and Cholinesterase and Neurodegenerative Diseases (10 papers). Prabha Garg collaborates with scholars based in India, Sweden and United States. Prabha Garg's co-authors include Rajender Kumar, Jitender Verma, Pawan Gupta, Navneet Kumar, Hardeep Sandhu, Nilanjan Roy, Mahesh C. Sharma, Inder Pal Singh, Prasad V. Bharatam and Anju Sharma and has published in prestigious journals such as Nucleic Acids Research, The Journal of Physical Chemistry B and Bioresource Technology.

In The Last Decade

Prabha Garg

99 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
Prabha Garg India 24 681 448 251 180 164 111 1.6k
Karthikeyan Muthusamy India 20 632 0.9× 340 0.8× 167 0.7× 134 0.7× 108 0.7× 138 1.4k
Vivek K. Vyas India 20 726 1.1× 352 0.8× 335 1.3× 182 1.0× 87 0.5× 86 1.4k
Andrea Volkamer Germany 20 1.4k 2.0× 936 2.1× 221 0.9× 203 1.1× 145 0.9× 55 2.1k
Vinícius Gonçalves Maltarollo Brazil 21 841 1.2× 799 1.8× 447 1.8× 152 0.8× 97 0.6× 110 1.9k
Peng Sang China 17 1.1k 1.7× 382 0.9× 239 1.0× 92 0.5× 123 0.8× 73 2.1k
Adam Yasgar United States 25 1.4k 2.1× 540 1.2× 271 1.1× 179 1.0× 286 1.7× 51 2.4k
H. C. Stephen Chan China 20 983 1.4× 508 1.1× 244 1.0× 84 0.5× 80 0.5× 33 1.9k
Sven B. Schreiber Germany 5 895 1.3× 513 1.1× 303 1.2× 185 1.0× 126 0.8× 5 1.7k
Sarah Naomi Bolz Germany 8 757 1.1× 300 0.7× 249 1.0× 146 0.8× 137 0.8× 12 1.4k
Katrin Stierand Germany 10 677 1.0× 304 0.7× 230 0.9× 134 0.7× 97 0.6× 15 1.2k

Countries citing papers authored by Prabha Garg

Since Specialization
Citations

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

Fields of papers citing papers by Prabha Garg

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Prabha Garg

This figure shows the co-authorship network connecting the top 25 collaborators of Prabha Garg. A scholar is included among the top collaborators of Prabha Garg 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 Prabha Garg. Prabha Garg 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.
Garg, Prabha, et al.. (2025). Can LLMs revolutionize text mining in chemistry? A comparative study with domain-specific tools. Computer Standards & Interfaces. 94. 103997–103997. 3 indexed citations
2.
Wani, Mushtaq Ahmad, A. Banerjee, & Prabha Garg. (2025). Computer-aided drug design approaches for the identification of potent inhibitors targeting elongation factor G of Mycobacterium tuberculosis. Journal of Molecular Graphics and Modelling. 136. 108954–108954.
3.
Sharma, Anju, et al.. (2025). Enzyme classification integrating LSTM and Prot-BERT sequence encoding. Applied Soft Computing. 184. 113774–113774. 1 indexed citations
4.
5.
Kumar, Rajender, Vishant Mahendra Boradia, Himanshu Malhotra, et al.. (2024). Stoichiometry of ligand binding and role of C‐terminal lysines in Mycobacterium tuberculosis and human GAPDH multifunctionality. FEBS Journal. 291(23). 5236–5255.
6.
Bharti, Prahalad Singh, Prabhat Kumar, Brijesh Singh Chauhan, et al.. (2024). Rhodanine composite fluorescence probes to detect pathological hallmarks in Alzheimer's disease models. Sensors and Actuators B Chemical. 407. 135364–135364. 11 indexed citations
7.
Sharma, Anju, Rajnish Kumar, Garima Yadav, & Prabha Garg. (2023). Artificial intelligence in intestinal polyp and colorectal cancer prediction. Cancer Letters. 565. 216238–216238. 23 indexed citations
8.
Ganugula, Raghu, et al.. (2023). Rationally Designed Naringenin-Conjugated Polyester Nanoparticles Enable Folate Receptor-Mediated Peroral Delivery of Insulin. ACS Applied Materials & Interfaces. 15(39). 45651–45657. 9 indexed citations
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11.
Garg, Prabha, et al.. (2018). In vitro and in vivo metabolic investigation of the Palbociclib by UHPLC-Q-TOF/MS/MS and in silico toxicity studies of its metabolites. Journal of Pharmaceutical and Biomedical Analysis. 157. 59–74. 36 indexed citations
12.
Sandhu, Hardeep, et al.. (2018). Predicting Inhibitors for Multidrug Resistance Associated Protein-2 Transporter by Machine Learning Approach. Combinatorial Chemistry & High Throughput Screening. 21(8). 557–566. 7 indexed citations
13.
14.
Kalariya, Pradipbhai D., et al.. (2017). Identification and characterization of vilazodone metabolites in rats and microsomes by ultrahigh‐performance liquid chromatography/quadrupole time‐of‐flight tandem mass spectrometry. Rapid Communications in Mass Spectrometry. 31(23). 1974–1984. 14 indexed citations
15.
Kalariya, Pradipbhai D., et al.. (2017). Characterization of forced degradation products of canagliflozine by liquid chromatography/quadrupole time‐of‐flight tandem mass spectrometry and in silico toxicity predictions. Rapid Communications in Mass Spectrometry. 32(3). 212–220. 9 indexed citations
16.
Kalariya, Pradipbhai D., et al.. (2017). Identification and characterization of fluvastatin metabolites in rats by UHPLC/Q‐TOF/MS/MS and in silico toxicological screening of the metabolites. Journal of Mass Spectrometry. 52(5). 296–314. 7 indexed citations
17.
18.
Sharma, Mahesh C., et al.. (2016). An improved approach for predicting drug–target interaction: proteochemometrics to molecular docking. Molecular BioSystems. 12(3). 1006–1014. 33 indexed citations
19.
Garg, Prabha, et al.. (2014). Role of breast cancer resistance protein (BCRP) as active efflux transporter on blood-brain barrier (BBB) permeability. Molecular Diversity. 19(1). 163–172. 29 indexed citations
20.
Garg, Prabha, et al.. (2007). QSAR modeling of CCR5 receptor antagonists using artificial neural network. 192–196. 1 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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