Om Silakari

671 total citations
32 papers, 446 citations indexed

About

Om Silakari is a scholar working on Computational Theory and Mathematics, Organic Chemistry and Molecular Biology. According to data from OpenAlex, Om Silakari has authored 32 papers receiving a total of 446 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computational Theory and Mathematics, 13 papers in Organic Chemistry and 11 papers in Molecular Biology. Recurrent topics in Om Silakari's work include Computational Drug Discovery Methods (16 papers), Synthesis and biological activity (10 papers) and Pharmacogenetics and Drug Metabolism (5 papers). Om Silakari is often cited by papers focused on Computational Drug Discovery Methods (16 papers), Synthesis and biological activity (10 papers) and Pharmacogenetics and Drug Metabolism (5 papers). Om Silakari collaborates with scholars based in India, Slovakia and Portugal. Om Silakari's co-authors include Yogita Bansal, Maninder Kaur, Gaganpreet Kaur, Pankaj Kumar Singh, Bharti Sapra, Malkeet Singh Bahia, Shalki Choudhary, Himanshu Verma, Himanshu Verma and Manjinder Singh and has published in prestigious journals such as International Journal of Pharmaceutics, European Journal of Medicinal Chemistry and Bioorganic & Medicinal Chemistry Letters.

In The Last Decade

Om Silakari

31 papers receiving 438 citations

Peers

Om Silakari

PHARM

CTM

ONCOL

Jaime Mella Chile

Annalisa Maruca Italy

Farghaly A. Omar Egypt

Santosh N. Mokale India

Vinod Devaraji India

Vinod G. Ugale India

Mohamed Elagawany Egypt

Jana Mandíková Czechia

Kavita Bhagat India

Mohd Haneef India

Jaime Mella Chile

Om Silakari

338 ×1.5

262 ×1.4

124 ×1.1

PHARM

144 ×1.3

CTM

43 ×1.0

ONCOL

Citations per year, relative to Om Silakari Om Silakari (= 1×) peers Jaime Mella

Countries citing papers authored by Om Silakari

Since Specialization

Citations

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

Fields of papers citing papers by Om Silakari

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Om Silakari

This figure shows the co-authorship network connecting the top 25 collaborators of Om Silakari. A scholar is included among the top collaborators of Om Silakari 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 Om Silakari. Om Silakari 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

Parveen, Shama, et al.. (2024). First report on QSAR modelling for chemical penetration enhancement ratio (ER) of different FDA-approved drugs in Poloxamer 407: A next step towards better skin permeability of drugs. International Journal of Pharmaceutics. 669. 125083–125083. 1 indexed citations

Kumar, Manoj, et al.. (2024). Leveraging machine learning to predict drug permeation: impact of menthol and limonene as enhancers. Molecular Diversity. 29(4). 3131–3146.

Sharma, Himani, Himanshu Verma, Bhavna Sharma, et al.. (2022). In silico guided designing of optimized benzochalcones derivatives as potent CYP1B1 inhibitors: An integrated in vitro and ONIOM study. Journal of Molecular Graphics and Modelling. 119. 108390–108390. 4 indexed citations

Sapra, Bharti, et al.. (2022). Molecular Docking, Dynamics, and WaterSwap Analysis to Identify Anti-aggregating Agents of Insulin and IFN-β. Applied Biochemistry and Biotechnology. 194(7). 3261–3279. 1 indexed citations

Sapra, Bharti, et al.. (2022). 3D-QSAR assisted identification of selective CYP1B1 inhibitors: an effective bioisosteric replacement/molecular docking/electrostatic complementarity analysis. Molecular Diversity. 27(6). 2673–2693. 4 indexed citations

Verma, Himanshu, et al.. (2021). Multiple machine learning, molecular docking, and ADMET screening approach for identification of selective inhibitors of CYP1B1. Journal of Biomolecular Structure and Dynamics. 40(17). 7975–7990. 26 indexed citations

Singh, Pankaj Kumar, Dasharath Chaudhari, Sanyog Jain, & Om Silakari. (2019). Structure based designing of triazolopyrimidone-based reversible inhibitors for kinases involved in NSCLC. Bioorganic & Medicinal Chemistry Letters. 29(13). 1565–1571. 10 indexed citations

Singh, Pankaj Kumar, et al.. (2018). Chemical classes targeting energy supplying GyrB domain of Mycobacterium tuberculosis. Tuberculosis. 113. 43–54. 17 indexed citations

Silakari, Om, et al.. (2018). Benzimidazole scaffold based hybrid molecules for various inflammatory targets: Synthesis and evaluation. Bioorganic Chemistry. 80. 24–35. 34 indexed citations

10.

Choudhary, Shalki, Om Silakari, & Pankaj Kumar Singh. (2018). New Insights into Chemistry and Biological Potential of Thiazine Scaffold: A Review. Mini-Reviews in Medicinal Chemistry. 18. 2 indexed citations

11.

Silakari, Om, et al.. (2017). Multiple Target-Centric Strategy to Tame Inflammation. Future Medicinal Chemistry. 9(12). 1361–1376. 22 indexed citations

12.

Chadha, Navriti, et al.. (2014). Imidazo[1,2-a]pyrazine inhibitors of phosphoinositide 3-kinase alpha (PI3Kα): 3D-QSAR analysis utilizing the Hybrid Monte Carlo algorithm to refine receptor-ligand complexes for molecular alignment. SAR and QSAR in environmental research. 25(3). 221–247. 2 indexed citations

13.

Bansal, Yogita & Om Silakari. (2014). Multifunctional compounds: Smart molecules for multifactorial diseases. European Journal of Medicinal Chemistry. 76. 31–42. 171 indexed citations

14.

Kaur, Gaganpreet, Maninder Kaur, & Om Silakari. (2014). Benzimidazoles: An Ideal Privileged Drug Scaffold for the Design of Multitargeted Anti-inflammatory Ligands. Mini-Reviews in Medicinal Chemistry. 14(9). 747–767. 35 indexed citations

15.

Chadha, Navriti, et al.. (2014). Pharmacophore and docking-based virtual screening approach for the design of new dual inhibitors of Janus kinase 1 and Janus kinase 2. SAR and QSAR in environmental research. 25(8). 617–636. 4 indexed citations

16.

Bansal, Yogita & Om Silakari. (2014). 2-Aminobenzimidazole conjugates of NSAIDS: novel compounds with immunomodulatory, anti-inflammatory and antioxidant actions. Medicinal Chemistry Research. 24(3). 1170–1179. 6 indexed citations

17.

Singh, Baldev, et al.. (2013). A Novel Combined Pharmacophore Mapping and Quantitative Structure Selectivity Relationship Analysis for the Development of Potent and Selective Human Aldose Reductase Inhibitors. Current Computer - Aided Drug Design. 9(3). 371–384. 1 indexed citations

18.

Kumar, Rajesh, et al.. (2011). Synthesis and Antibacterial Activity of Benzotriazole Substituted Acridines. 5(3). 193–199. 20 indexed citations

19.

Silakari, Om, et al.. (2008). Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of various benzodiazepine analogues of γ-secretase inhibitors. Journal of Molecular Modeling. 15(4). 343–348. 4 indexed citations

20.

Silakari, Om, et al.. (2007). Pharmacophore mapping of diverse classes of farnesyltransferase inhibitors. Bioorganic & Medicinal Chemistry Letters. 17(6). 1594–1600. 20 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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