Subhash Mohan Agarwal

1.9k total citations
70 papers, 1.4k citations indexed

About

Subhash Mohan Agarwal is a scholar working on Molecular Biology, Computational Theory and Mathematics and Organic Chemistry. According to data from OpenAlex, Subhash Mohan Agarwal has authored 70 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 21 papers in Computational Theory and Mathematics and 15 papers in Organic Chemistry. Recurrent topics in Subhash Mohan Agarwal's work include Computational Drug Discovery Methods (21 papers), Synthesis and biological activity (11 papers) and Amoebic Infections and Treatments (8 papers). Subhash Mohan Agarwal is often cited by papers focused on Computational Drug Discovery Methods (21 papers), Synthesis and biological activity (11 papers) and Amoebic Infections and Treatments (8 papers). Subhash Mohan Agarwal collaborates with scholars based in India, Spain and United Kingdom. Subhash Mohan Agarwal's co-authors include Gajendra P. S. Raghava, Harinder Singh, Amir Azam, Amir Azam, Prajwal P. Nandekar, Garima Sharma, Mohammad Abid, Fernando Avecilla, Deepak Singla and Salahuddin Attar and has published in prestigious journals such as Nucleic Acids Research, PLoS ONE and Biochemical and Biophysical Research Communications.

In The Last Decade

Subhash Mohan Agarwal

70 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Subhash Mohan Agarwal India 22 689 396 337 199 148 70 1.4k
Prabha Garg India 24 681 1.0× 448 1.1× 251 0.7× 164 0.8× 73 0.5× 111 1.6k
Sarah Naomi Bolz Germany 8 757 1.1× 300 0.8× 249 0.7× 137 0.7× 77 0.5× 12 1.4k
Youyou Tu China 13 962 1.4× 282 0.7× 252 0.7× 177 0.9× 117 0.8× 29 2.4k
Sven B. Schreiber Germany 5 895 1.3× 513 1.3× 303 0.9× 126 0.6× 68 0.5× 5 1.7k
Keng‐Chang Tsai Taiwan 27 975 1.4× 406 1.0× 555 1.6× 138 0.7× 263 1.8× 103 2.2k
Hongmao Sun United States 26 1.3k 1.8× 974 2.5× 337 1.0× 279 1.4× 128 0.9× 48 2.2k
Massimo Baroni Italy 25 1.1k 1.6× 869 2.2× 374 1.1× 188 0.9× 81 0.5× 63 2.1k
Eva S. Istvan United States 17 1.3k 1.8× 558 1.4× 169 0.5× 277 1.4× 260 1.8× 24 2.9k
Yingke He Singapore 13 450 0.7× 183 0.5× 143 0.4× 138 0.7× 178 1.2× 23 1.3k
Shiow‐Ju Lee Taiwan 29 977 1.4× 166 0.4× 497 1.5× 206 1.0× 111 0.8× 59 2.3k

Countries citing papers authored by Subhash Mohan Agarwal

Since Specialization
Citations

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

Fields of papers citing papers by Subhash Mohan Agarwal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Subhash Mohan Agarwal. 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 Subhash Mohan Agarwal. The network helps show where Subhash Mohan Agarwal may publish in the future.

Co-authorship network of co-authors of Subhash Mohan Agarwal

This figure shows the co-authorship network connecting the top 25 collaborators of Subhash Mohan Agarwal. A scholar is included among the top collaborators of Subhash Mohan Agarwal 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 Subhash Mohan Agarwal. Subhash Mohan Agarwal 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.
Agarwal, Subhash Mohan, et al.. (2025). PhyIndBC: Development of a machine learning tool for screening of potential breast cancer inhibitors from phytochemicals. Current Plant Biology. 42. 100435–100435. 1 indexed citations
2.
Sharma, Shiwani, et al.. (2024). Unveiling the potential of novel 5α-reductase inhibitors via ligand based drug design, molecular docking and ADME predictions to manage BPH. Journal of Molecular Structure. 1320. 139547–139547. 4 indexed citations
3.
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5.
Singh, Pushpendra, Anuj Kumar, Robin Marwal, et al.. (2023). Genome sequencing of SARS-CoV-2 omicron variants in Delhi reveals alterations in immunogenic regions in spike glycoprotein. Frontiers in Immunology. 14. 1209513–1209513. 4 indexed citations
6.
Nandekar, Prajwal P., et al.. (2023). A systematic pipeline of protein structure selection for computer‐aided drug discovery: A case study on T790M/L858R mutant EGFR structures. Protein Science. 32(9). e4740–e4740. 3 indexed citations
7.
Agarwal, Subhash Mohan, et al.. (2023). Recent advances in the area of plant-based anti-cancer drug discovery using computational approaches. Molecular Diversity. 28(2). 901–925. 33 indexed citations
8.
Borkar, Roshan M., et al.. (2023). A pharmacokinetic study to correlate the hypoglycemic effect of phlorizin in rats: Identification of metabolites as inhibitors of sodium/glucose cotransporters. Journal of Mass Spectrometry. 58(8). e4964–e4964. 3 indexed citations
9.
Agarwal, Subhash Mohan, et al.. (2022). Elucidation of Increased Cervical Cancer Risk Due to Polymorphisms in XRCC1 (R399Q and R194W), ERCC5 (D1104H), and NQO1 (P187S). Reproductive Sciences. 30(4). 1118–1132. 3 indexed citations
10.
Agarwal, Subhash Mohan, Prajwal P. Nandekar, & Ravi Saini. (2022). Computational identification of natural product inhibitors against EGFR double mutant (T790M/L858R) by integrating ADMET, machine learning, molecular docking and a dynamics approach. RSC Advances. 12(26). 16779–16789. 20 indexed citations
11.
Saini, Ravi & Subhash Mohan Agarwal. (2021). EGFRisopred: a machine learning-based classification model for identifying isoform-specific inhibitors against EGFR and HER2. Molecular Diversity. 26(3). 1531–1543. 5 indexed citations
12.
Saini, Ravi, et al.. (2020). TMLRpred: A machine learning classification model to distinguish reversible EGFR double mutant inhibitors. Chemical Biology & Drug Design. 96(3). 921–930. 7 indexed citations
13.
Agarwal, Subhash Mohan, et al.. (2019). QSAR of clinically important EGFR mutant L858R/T790M pyridinylimidazole inhibitors. Chemical Biology & Drug Design. 94(1). 1306–1315. 10 indexed citations
14.
Sharma, Shilpa, et al.. (2019). ADMET Profiling of Geographically Diverse Phytochemical Using Chemoinformatic Tools. Future Medicinal Chemistry. 12(1). 69–87. 50 indexed citations
15.
Agarwal, Subhash Mohan, et al.. (2019). Exploring structural features of EGFR–HER2 dual inhibitors as anti-cancer agents using G-QSAR approach. Journal of Receptors and Signal Transduction. 39(3). 243–252. 1 indexed citations
16.
Agarwal, Subhash Mohan, et al.. (2018). Insight into structural requirements of antiamoebic flavonoids: 3D‐QSAR and G‐QSAR studies. Chemical Biology & Drug Design. 92(4). 1743–1749. 6 indexed citations
17.
Sharma, Shilpa, et al.. (2018). Pharmacokinetic profiling of anticancer phytocompounds using computational approach. Phytochemical Analysis. 29(6). 559–568. 20 indexed citations
18.
Malik, Md. Zubbair, et al.. (2016). Control of apoptosis by SMAR 1. Molecular BioSystems. 13(2). 350–362. 14 indexed citations
19.
Alam, Md. Jahoor, et al.. (2013). Switching p 53 states by calcium: dynamics and interaction of stress systems. Molecular BioSystems. 9(3). 508–521. 9 indexed citations
20.
Bakshi, Vasudha, et al.. (2004). Niosomes of primaquine: Effect of sorbitan esters (spans) on the vesicular physical characteristics. INDIAN DRUGS. 41(2). 101–103. 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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