Vaneet Saini

872 total citations
21 papers, 734 citations indexed

About

Vaneet Saini is a scholar working on Organic Chemistry, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Vaneet Saini has authored 21 papers receiving a total of 734 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Organic Chemistry, 8 papers in Materials Chemistry and 7 papers in Computational Theory and Mathematics. Recurrent topics in Vaneet Saini's work include Computational Drug Discovery Methods (7 papers), Catalytic C–H Functionalization Methods (6 papers) and Machine Learning in Materials Science (6 papers). Vaneet Saini is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Catalytic C–H Functionalization Methods (6 papers) and Machine Learning in Materials Science (6 papers). Vaneet Saini collaborates with scholars based in India, United States and Nepal. Vaneet Saini's co-authors include Matthew S. Sigman, Benjamin J. Stokes, F. Dean Toste, Qiaofeng Wang, Ranjan Jana, Manuel Orlandi, Eiji Yamamoto, Longyan Liao, Margaret J. Hilton and Talita de A. Fernandes and has published in prestigious journals such as Journal of the American Chemical Society, Angewandte Chemie International Edition and SHILAP Revista de lepidopterología.

In The Last Decade

Vaneet Saini

19 papers receiving 727 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Vaneet Saini India 11 599 211 85 79 75 21 734
Rupert S. J. Proctor United Kingdom 10 1.6k 2.6× 176 0.8× 75 0.9× 107 1.4× 158 2.1× 13 1.7k
Rajat Maji Germany 14 663 1.1× 212 1.0× 49 0.6× 73 0.9× 45 0.6× 17 756
Nobuya Tsuji Japan 12 577 1.0× 212 1.0× 64 0.8× 86 1.1× 42 0.6× 25 692
Mukesh M. Jotani Malaysia 11 647 1.1× 526 2.5× 113 1.3× 66 0.8× 26 0.3× 104 905
Matthias Gotta Germany 8 1.0k 1.7× 165 0.8× 47 0.6× 134 1.7× 84 1.1× 11 1.1k
Kuangbiao Liao China 12 1.2k 1.9× 307 1.5× 109 1.3× 57 0.7× 74 1.0× 22 1.3k
Efrén V. Garcı́a-Báez Mexico 14 367 0.6× 196 0.9× 77 0.9× 83 1.1× 17 0.2× 81 565
Ş. Işık Türkiye 14 829 1.4× 144 0.7× 81 1.0× 142 1.8× 13 0.2× 111 991
Vaibhav P. Mehta Belgium 22 1.7k 2.8× 213 1.0× 63 0.7× 194 2.5× 170 2.3× 42 1.8k
Susana Rojas‐Lima Mexico 16 484 0.8× 167 0.8× 124 1.5× 120 1.5× 14 0.2× 56 682

Countries citing papers authored by Vaneet Saini

Since Specialization
Citations

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

Fields of papers citing papers by Vaneet Saini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Vaneet Saini

This figure shows the co-authorship network connecting the top 25 collaborators of Vaneet Saini. A scholar is included among the top collaborators of Vaneet Saini 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 Vaneet Saini. Vaneet Saini 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.
Saini, Vaneet, et al.. (2025). A machine learning-driven prediction of Hammett constants using quantum chemical and structural descriptors. Physical Chemistry Chemical Physics. 27(24). 12951–12962. 2 indexed citations
2.
Saini, Vaneet, et al.. (2024). Leveraging graph neural networks to predict Hammett’s constants for benzoic acid derivatives. SHILAP Revista de lepidopterología. 2(2). 100079–100079. 2 indexed citations
3.
Saini, Vaneet, et al.. (2024). Comparative study of luminescent Cd-MOF and Cd-MOF@HNT nanomaterials for the detection of hydroxyl-functionalized nitroaromatic compounds. New Journal of Chemistry. 48(47). 20018–20033. 7 indexed citations
4.
Saini, Vaneet, et al.. (2023). Predicting the ET(30) parameter of organic solvents via machine learning. Chemical Physics Letters. 826. 140672–140672. 9 indexed citations
5.
6.
Saini, Vaneet. (2022). Machine learning prediction of empirical polarity using SMILES encoding of organic solvents. Molecular Diversity. 27(5). 2331–2343. 18 indexed citations
7.
Saini, Vaneet, et al.. (2022). A machine learning approach for predicting the empirical polarity of organic solvents. New Journal of Chemistry. 46(35). 16981–16989. 11 indexed citations
8.
Kaur, Manpreet, Vishal Mutreja, Deepak B. Salunke, et al.. (2022). Synthesis of quinoline based molecular probes for detection of nitric oxide. Dyes and Pigments. 201. 110226–110226. 7 indexed citations
9.
Saini, Vaneet. (2022). A machine learning approach for predicting the fluorination strength of electrophilic fluorinating reagents. Physical Chemistry Chemical Physics. 24(43). 26802–26812. 5 indexed citations
10.
11.
Singh, Harjinder & Vaneet Saini. (2022). Development, synthesis, computational and in silico investigations of Pd(II)-catalyzed aryl fluorinated and hydroxylated sulfonamides. Journal of Molecular Structure. 1266. 133481–133481. 2 indexed citations
12.
Saini, Vaneet, et al.. (2021). A machine learning approach for predicting the nucleophilicity of organic molecules. Physical Chemistry Chemical Physics. 24(3). 1821–1829. 19 indexed citations
13.
Vashisht, Devika, Shikha Sharma, Rakesh Kumar, et al.. (2020). Dehydroacetic acid derived Schiff base as selective and sensitive colorimetric chemosensor for the detection of Cu(II) ions in aqueous medium. Microchemical Journal. 155. 104705–104705. 43 indexed citations
14.
Thornbury, Richard T., Vaneet Saini, Talita de A. Fernandes, et al.. (2017). The development and mechanistic investigation of a palladium-catalyzed 1,3-arylfluorination of chromenes. Chemical Science. 8(4). 2890–2897. 77 indexed citations
15.
Yamamoto, Eiji, Margaret J. Hilton, Manuel Orlandi, et al.. (2016). Development and Analysis of a Pd(0)-Catalyzed Enantioselective 1,1-Diarylation of Acrylates Enabled by Chiral Anion Phase Transfer. Journal of the American Chemical Society. 138(49). 15877–15880. 114 indexed citations
16.
Saini, Vaneet, et al.. (2015). Synthesis of Highly Functionalized Tri- and Tetrasubstituted Alkenes via Pd-Catalyzed 1,2-Hydrovinylation of Terminal 1,3-Dienes. Journal of the American Chemical Society. 137(2). 608–611. 97 indexed citations
17.
Saini, Vaneet, Benjamin J. Stokes, & Matthew S. Sigman. (2013). Transition‐Metal‐Catalyzed Laboratory‐Scale Carbon–Carbon Bond‐Forming Reactions of Ethylene. Angewandte Chemie International Edition. 52(43). 11206–11220. 99 indexed citations
18.
Saini, Vaneet, Longyan Liao, Qiaofeng Wang, Ranjan Jana, & Matthew S. Sigman. (2013). Pd(0)-Catalyzed 1,1-Diarylation of Ethylene and Allylic Carbonates. Organic Letters. 15(19). 5008–5011. 93 indexed citations
19.
Saini, Vaneet, Benjamin J. Stokes, & Matthew S. Sigman. (2013). Übergangsmetallkatalysierte C‐C‐Kupplungen mit Ethylen im Labormaßstab. Angewandte Chemie. 125(43). 11414–11429. 21 indexed citations
20.
Saini, Vaneet & Matthew S. Sigman. (2012). Palladium-Catalyzed 1,1-Difunctionalization of Ethylene. Journal of the American Chemical Society. 134(28). 11372–11375. 103 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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