Rachel E. Davis

1.3k total citations · 1 hit paper
16 papers, 970 citations indexed

About

Rachel E. Davis is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Rachel E. Davis has authored 16 papers receiving a total of 970 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 4 papers in Immunology and 3 papers in Oncology. Recurrent topics in Rachel E. Davis's work include Heat shock proteins research (8 papers), Computational Drug Discovery Methods (3 papers) and ATP Synthase and ATPases Research (3 papers). Rachel E. Davis is often cited by papers focused on Heat shock proteins research (8 papers), Computational Drug Discovery Methods (3 papers) and ATP Synthase and ATPases Research (3 papers). Rachel E. Davis collaborates with scholars based in United States, United Kingdom and France. Rachel E. Davis's co-authors include Brian S. J. Blagg, Carolyn Discafani, Ramaswamy Nilakantan, Allan Wissner, Nellie Mamuya, Lee M. Greenberger, Bernard D. Johnson, Ru Shen, Hwei‐Ru Tsou and Marvin F. Reich and has published in prestigious journals such as Journal of Biological Chemistry, Angewandte Chemie International Edition and Nature Communications.

In The Last Decade

Rachel E. Davis

15 papers receiving 953 citations

Hit Papers

A first-in-class polymerase theta inhibitor selectively t... 2021 2026 2022 2024 2021 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. A first-in-class polymerase theta inhibitor selectively targets homologous-recombination-deficient tumors
    2021 226 citations Jia Zhou, Camille Gelot et al. Nature Cancer profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rachel E. Davis United States 11 606 394 273 111 94 16 970
Carsten Schultz‐Fademrecht Germany 20 804 1.3× 452 1.1× 469 1.7× 87 0.8× 34 0.4× 38 1.2k
Yeuan Ting Lee Malaysia 6 498 0.8× 300 0.8× 197 0.7× 139 1.3× 42 0.4× 9 1.0k
Bernard Barlaam United Kingdom 17 448 0.7× 256 0.6× 306 1.1× 101 0.9× 63 0.7× 40 811
Mingfeng Yu Australia 21 674 1.1× 443 1.1× 347 1.3× 273 2.5× 38 0.4× 53 1.3k
Benedict‐Tilman Berger Germany 19 590 1.0× 197 0.5× 207 0.8× 49 0.4× 60 0.6× 50 904
Michelle L. Kraus United States 7 466 0.8× 447 1.1× 96 0.4× 124 1.1× 63 0.7× 9 1.1k
Krishna G. Vijayendran United States 5 604 1.0× 298 0.8× 195 0.7× 72 0.6× 20 0.2× 5 847
Thomas W. Gero United States 14 665 1.1× 337 0.9× 287 1.1× 240 2.2× 28 0.3× 24 947
Angelo Aguilar United States 17 1.3k 2.1× 699 1.8× 419 1.5× 74 0.7× 39 0.4× 28 1.8k
Guoshun Luo China 17 423 0.7× 274 0.7× 301 1.1× 87 0.8× 21 0.2× 46 858

Countries citing papers authored by Rachel E. Davis

Since Specialization
Citations

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

Fields of papers citing papers by Rachel E. Davis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rachel E. Davis. 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 Rachel E. Davis. The network helps show where Rachel E. Davis may publish in the future.

Co-authorship network of co-authors of Rachel E. Davis

This figure shows the co-authorship network connecting the top 25 collaborators of Rachel E. Davis. A scholar is included among the top collaborators of Rachel E. Davis 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 Rachel E. Davis. Rachel E. Davis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Leysen, S., Rebecca J. Burnley, Elizabeth Rodríguez, et al.. (2021). A Structural Study of the Cytoplasmic Chaperone Effect of 14-3-3 Proteins on Ataxin-1. Journal of Molecular Biology. 433(19). 167174–167174. 8 indexed citations
2.
McMillan, David, C. Martinez-Fleites, John Porter, et al.. (2021). Structural insights into the disruption of TNF-TNFR1 signalling by small molecules stabilising a distorted TNF. Nature Communications. 12(1). 582–582. 63 indexed citations
3.
Zhou, Jia, Camille Gelot, Constantia Pantelidou, et al.. (2021). A first-in-class polymerase theta inhibitor selectively targets homologous-recombination-deficient tumors. Nature Cancer. 2(6). 598–610. 226 indexed citations breakdown →
4.
Mishra, Sanket J., Anuj Khandelwal, Monimoy Banerjee, et al.. (2021). Selective Inhibition of the Hsp90α Isoform. Angewandte Chemie. 133(19). 10641–10645.
5.
Esser, Tim K., Thomas D. Newport, Francesco Fiorentino, et al.. (2021). NaViA: a program for the visual analysis of complex mass spectra. Bioinformatics. 37(24). 4876–4878. 12 indexed citations
6.
Mishra, Sanket J., Anuj Khandelwal, Monimoy Banerjee, et al.. (2021). Selective Inhibition of the Hsp90α Isoform. Angewandte Chemie International Edition. 60(19). 10547–10551. 57 indexed citations
7.
O’Connell, James P., John Porter, Stephen Rapecki, et al.. (2019). Small molecules that inhibit TNF signalling by stabilising an asymmetric form of the trimer. Nature Communications. 10(1). 5795–5795. 81 indexed citations
8.
Jayaraj, Abhilash, Vinay Kumar, Brian S. J. Blagg, et al.. (2019). Stimulation of heat shock protein 90 chaperone function through binding of a novobiocin analog KU-32. Journal of Biological Chemistry. 294(16). 6450–6467. 11 indexed citations
9.
Zhang, Zheng, Monimoy Banerjee, Rachel E. Davis, & Brian S. J. Blagg. (2019). Mitochondrial-targeted Hsp90 C-terminal inhibitors manifest anti-proliferative activity. Bioorganic & Medicinal Chemistry Letters. 29(22). 126676–126676. 12 indexed citations
10.
Davis, Rachel E., et al.. (2018). Molecular insights into the interaction of Hsp90 with allosteric inhibitors targeting the C-terminal domain. MedChemComm. 9(8). 1323–1331. 16 indexed citations
11.
Forsberg, Leah K., et al.. (2018). Exploiting polarity and chirality to probe the Hsp90 C-terminus. Bioorganic & Medicinal Chemistry. 26(12). 3096–3110. 5 indexed citations
12.
Subramanian, Chitra, Kevin J. Kovatch, Michael W. Sim, et al.. (2017). Novel C-Terminal Heat Shock Protein 90 Inhibitors (KU711 and Ku757) Are Effective in Targeting Head and Neck Squamous Cell Carcinoma Cancer Stem cells. Neoplasia. 19(12). 1003–1011. 28 indexed citations
13.
Davis, Rachel E., Zheng Zhang, & Brian S. J. Blagg. (2017). A scaffold merging approach to Hsp90 C-terminal inhibition: synthesis and evaluation of a chimeric library. MedChemComm. 8(3). 593–598. 7 indexed citations
14.
Clevenger, William, Tamás Szabó, Leena Ackermann, et al.. (2004). Ectopic expression of the human adenine nucleotide translocase, isoform 3 (ANT-3). Characterization of ligand binding properties. Mitochondrion. 5(1). 1–13. 5 indexed citations
15.
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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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