Tushar Joshi

2.0k total citations
42 papers, 1.0k citations indexed

About

Tushar Joshi is a scholar working on Computational Theory and Mathematics, Infectious Diseases and Molecular Biology. According to data from OpenAlex, Tushar Joshi has authored 42 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computational Theory and Mathematics, 9 papers in Infectious Diseases and 9 papers in Molecular Biology. Recurrent topics in Tushar Joshi's work include Computational Drug Discovery Methods (17 papers), Synthesis and biological activity (7 papers) and SARS-CoV-2 and COVID-19 Research (6 papers). Tushar Joshi is often cited by papers focused on Computational Drug Discovery Methods (17 papers), Synthesis and biological activity (7 papers) and SARS-CoV-2 and COVID-19 Research (6 papers). Tushar Joshi collaborates with scholars based in India, Saudi Arabia and Bangladesh. Tushar Joshi's co-authors include Subhash Chandra, Tanuja Joshi, Priyanka Sharma, Shalini Mathpal, Hemlata Pundir, Veena Pande, Sushma Tamta, Wenping Zhang, Priyanka Sharma and Kalpana Bhatt and has published in prestigious journals such as PLoS ONE, Journal of Hazardous Materials and Scientific Reports.

In The Last Decade

Tushar Joshi

41 papers receiving 987 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tushar Joshi India 16 357 322 183 153 131 42 1.0k
Md. Abu Saleh Bangladesh 21 348 1.0× 519 1.6× 252 1.4× 188 1.2× 127 1.0× 84 1.3k
R.P. Vivek-Ananth India 11 349 1.0× 364 1.1× 136 0.7× 89 0.6× 107 0.8× 17 884
Mi Sun Kim South Korea 13 311 0.9× 361 1.1× 105 0.6× 272 1.8× 200 1.5× 32 1.3k
Md. Nazmul Hasan Bangladesh 21 191 0.5× 312 1.0× 181 1.0× 57 0.4× 98 0.7× 60 980
Karthikeyan Mohanraj India 7 322 0.9× 295 0.9× 122 0.7× 53 0.3× 101 0.8× 9 769
O.U. Orji Nigeria 12 134 0.4× 230 0.7× 124 0.7× 37 0.2× 77 0.6× 25 766
Chaofeng Lou China 9 559 1.6× 472 1.5× 85 0.5× 69 0.5× 155 1.2× 11 1.2k
Patrick Maduabuchi Aja Nigeria 19 164 0.5× 384 1.2× 418 2.3× 49 0.3× 181 1.4× 106 1.6k
Vinícius M. Alves United States 23 838 2.3× 587 1.8× 86 0.5× 97 0.6× 98 0.7× 53 1.5k
Pukar Khanal India 25 300 0.8× 431 1.3× 235 1.3× 61 0.4× 262 2.0× 90 1.5k

Countries citing papers authored by Tushar Joshi

Since Specialization
Citations

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

Fields of papers citing papers by Tushar Joshi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tushar Joshi

This figure shows the co-authorship network connecting the top 25 collaborators of Tushar Joshi. A scholar is included among the top collaborators of Tushar Joshi 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 Tushar Joshi. Tushar Joshi 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.
Tamhane, Vaijayanti A., et al.. (2025). In silico exploration of biosynthetic gene clusters in marine Streptomyces sp. and Nocardiopsis sp. from the western coast of India: Genome-based profiling using whole genome sequencing. Journal of Genetic Engineering and Biotechnology. 23(2). 100483–100483. 3 indexed citations
2.
Mathpal, Shalini, et al.. (2025). Machine learning and cheminformatics-based Identification of lichen-derived compounds targeting mutant PBP4R200L in Staphylococcus aureus. Molecular Diversity. 29(4). 3345–3370. 3 indexed citations
3.
4.
Joshi, Tushar, et al.. (2025). In-silico evaluation of Azadirachta indica-derived Daucosterol against key viral proteins of Ebolavirus using ML and MD simulations approach. Journal of Biological Physics. 51(1). 17–17. 2 indexed citations
5.
Joshi, Tushar, et al.. (2024). Functional metabolites of probiotic lactic acid bacteria in fermented dairy products. Food and Humanity. 3. 100341–100341. 14 indexed citations
6.
Joshi, Tushar, et al.. (2024). Identifying Novel Therapeutics for the Resistant Mutant “F533L” in PBP3 of Pseudomonas aeruginosa Using ML Techniques. ACS Omega. 9(26). 28046–28060. 15 indexed citations
7.
Joshi, Tushar, et al.. (2024). Designing novel inhibitor derivatives targeting SARS-CoV-2 Mpro enzyme: a deep learning and structure biology approach. Molecular Systems Design & Engineering. 9(10). 1063–1076. 3 indexed citations
8.
9.
Joshi, Tushar, et al.. (2023). Smart Billing System using IoT. International Journal of Scientific Research in Science and Technology. 856–860. 1 indexed citations
10.
Kumar, Ankit, Ashutosh Chauhan, Ruchi Badoni Semwal, et al.. (2022). Formulation and evaluation of SGLT2 inhibitory effect of a polyherbal mixture inspired from Ayurvedic system of medicine. Journal of Traditional and Complementary Medicine. 12(5). 477–487. 8 indexed citations
11.
Mathpal, Shalini, Tushar Joshi, Priyanka Sharma, Veena Pande, & Subhash Chandra. (2022). Assessment of activity of chalcone compounds as inhibitors of 3-chymotrypsin like protease (3CLPro) of SARS-CoV-2: in silico study. Structural Chemistry. 33(5). 1815–1831. 8 indexed citations
12.
Sharma, Priyanka, Indra D. Bhatt, Muthannan Andavar Ramakrishnan, et al.. (2022). Integrated Machine Learning and Chemoinformatics-Based Screening of Mycotic Compounds against Kinesin Spindle ProteinEg5 for Lung Cancer Therapy. Molecules. 27(5). 1639–1639. 11 indexed citations
13.
14.
Mathpal, Shalini, Priyanka Sharma, Tushar Joshi, et al.. (2022). Identification of Zinc-Binding Inhibitors of Matrix Metalloproteinase-9 to Prevent Cancer Through Deep Learning and Molecular Dynamics Simulation Approach. Frontiers in Molecular Biosciences. 9. 857430–857430. 15 indexed citations
15.
Joshi, Tushar, Priyanka Sharma, Shalini Mathpal, et al.. (2021). Computational investigation of drug bank compounds against 3C-like protease (3CLpro) of SARS-CoV-2 using deep learning and molecular dynamics simulation. Molecular Diversity. 26(4). 2243–2256. 15 indexed citations
16.
Joshi, Amit, et al.. (2020). Identification of potent Antigen 85C inhibitors of Mycobacterium tuberculosis via in-house lichen library and binding free energy studies Part-II. Journal of Molecular Graphics and Modelling. 103. 107822–107822. 5 indexed citations
17.
Joshi, Tanuja, Priyanka Sharma, Tushar Joshi, et al.. (2020). Structure-based screening of novel lichen compounds against SARS Coronavirus main protease (Mpro) as potentials inhibitors of COVID-19. Molecular Diversity. 25(3). 1665–1677. 59 indexed citations
18.
Joshi, Tushar, et al.. (2020). Equilibrium shape of misfitting precipitates with anisotropic elasticity and anisotropic interfacial energy. Modelling and Simulation in Materials Science and Engineering. 28(7). 75009–75009. 3 indexed citations
19.
20.
Pundir, Hemlata, Tanuja Joshi, Tushar Joshi, et al.. (2020). Using Chou’s 5-steps rule to study pharmacophore-based virtual screening of SARS-CoV-2 Mpro inhibitors. Molecular Diversity. 25(3). 1731–1744. 14 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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