Robert R. Lavieri

980 total citations
17 papers, 685 citations indexed

About

Robert R. Lavieri is a scholar working on Molecular Biology, Computational Theory and Mathematics and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Robert R. Lavieri has authored 17 papers receiving a total of 685 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 5 papers in Computational Theory and Mathematics and 4 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Robert R. Lavieri's work include Computational Drug Discovery Methods (5 papers), Nitric Oxide and Endothelin Effects (3 papers) and Protein Kinase Regulation and GTPase Signaling (3 papers). Robert R. Lavieri is often cited by papers focused on Computational Drug Discovery Methods (5 papers), Nitric Oxide and Endothelin Effects (3 papers) and Protein Kinase Regulation and GTPase Signaling (3 papers). Robert R. Lavieri collaborates with scholars based in United States, Russia and Denmark. Robert R. Lavieri's co-authors include Craig W. Lindsley, H. Alex Brown, Sarah Scott, Jana A. Lewis, Michelle D. Armstrong, Jill M. Pulley, David M. Aronoff, Anup P. Challa, Jana K. Shirey-Rice and Jason R. Buck and has published in prestigious journals such as Chemical Reviews, Nature Medicine and SHILAP Revista de lepidopterología.

In The Last Decade

Robert R. Lavieri

17 papers receiving 682 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Robert R. Lavieri United States 11 440 114 63 61 60 17 685
Ireos Filipuzzi Switzerland 13 687 1.6× 121 1.1× 63 1.0× 141 2.3× 74 1.2× 21 897
Olga V. Tikhonova Russia 17 473 1.1× 73 0.6× 34 0.5× 40 0.7× 62 1.0× 80 803
Stuart P. McElroy United Kingdom 17 462 1.1× 59 0.5× 136 2.2× 44 0.7× 57 0.9× 32 789
Heejun Hwang South Korea 7 462 1.1× 57 0.5× 45 0.7× 26 0.4× 48 0.8× 9 649
Ambuj Kumar India 16 627 1.4× 129 1.1× 50 0.8× 108 1.8× 97 1.6× 37 840
Shukie Ng Singapore 11 453 1.0× 117 1.0× 47 0.7× 21 0.3× 111 1.9× 11 737
Byung Hak Ha United States 17 646 1.5× 214 1.9× 57 0.9× 58 1.0× 218 3.6× 37 868
Tímea Polgár Hungary 13 377 0.9× 152 1.3× 44 0.7× 46 0.8× 61 1.0× 20 713
Jianping Liu China 16 491 1.1× 165 1.4× 30 0.5× 23 0.4× 75 1.3× 29 796
Kutbuddin S. Doctor United States 10 707 1.6× 56 0.5× 50 0.8× 51 0.8× 74 1.2× 14 963

Countries citing papers authored by Robert R. Lavieri

Since Specialization
Citations

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

Fields of papers citing papers by Robert R. Lavieri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Robert R. Lavieri. 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 Robert R. Lavieri. The network helps show where Robert R. Lavieri may publish in the future.

Co-authorship network of co-authors of Robert R. Lavieri

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

All Works

17 of 17 papers shown
1.
2.
Challa, Anup P., Xinnan Niu, Etoi Garrison, et al.. (2022). Medication history-wide association studies for pharmacovigilance of pregnant patients. SHILAP Revista de lepidopterología. 2(1). 115–115. 3 indexed citations
3.
Challa, Anup P., Rebecca N Jerome, Robert R. Lavieri, et al.. (2021). Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases. Frontiers in Genetics. 12. 707836–707836. 13 indexed citations
4.
Werfel, Thomas A., Donna J. Hicks, Bushra Rahman, et al.. (2020). Repurposing of a Thromboxane Receptor Inhibitor Based on a Novel Role in Metastasis Identified by Phenome-Wide Association Study. Molecular Cancer Therapeutics. 19(12). 2454–2464. 15 indexed citations
5.
Challa, Anup P., Robert R. Lavieri, Ethan S. Lippmann, et al.. (2020). EHRs could clarify drug safety in pregnant people. Nature Medicine. 26(6). 820–821. 2 indexed citations
6.
Challa, Anup P., Andrew L. Beam, Min Shen, et al.. (2020). Machine learning on drug-specific data to predict small molecule teratogenicity. Reproductive Toxicology. 95. 148–158. 18 indexed citations
7.
Challa, Anup P., Robert R. Lavieri, Jana K. Shirey-Rice, et al.. (2019). Systematically Prioritizing Candidates in Genome-Based Drug Repurposing. Assay and Drug Development Technologies. 17(8). 352–363. 13 indexed citations
8.
Pulley, Jill M., Jillian P. Rhoads, Rebecca N Jerome, et al.. (2019). Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data. The Annual Review of Pharmacology and Toxicology. 60(1). 333–352. 37 indexed citations
9.
Challa, Anup P., et al.. (2018). Systematically Prioritizing Targets in Genome-Based Drug Repurposing. 543–543. 3 indexed citations
10.
Pulley, Jill M., Jana K. Shirey-Rice, Robert R. Lavieri, et al.. (2017). Accelerating Precision Drug Development and Drug Repurposing by Leveraging Human Genetics. Assay and Drug Development Technologies. 15(3). 113–119. 30 indexed citations
11.
Pulley, Jill M., Rebecca N Jerome, Jana K. Shirey-Rice, et al.. (2017). When Enough Is Enough: Decision Criteria for Moving a Known Drug into Clinical Testing for a New Indication in the Absence of Preclinical Efficacy Data. Assay and Drug Development Technologies. 15(8). 354–361. 6 indexed citations
12.
Pulley, Jill M., Rebecca N Jerome, Martin L. Ogletree, et al.. (2017). Motivation for Launching a Cancer Metastasis Inhibition (CMI) Program. Targeted Oncology. 13(1). 61–68. 8 indexed citations
13.
Scott, Sarah, Matthew C. O’Reilly, Kyle A. Brown, et al.. (2014). Discovery of Desketoraloxifene Analogues as Inhibitors of Mammalian, Pseudomonas aeruginosa, and NAPE Phospholipase D Enzymes. ACS Chemical Biology. 10(2). 421–432. 36 indexed citations
14.
Lavieri, Robert R., et al.. (2011). Phospholipase D: Enzymology, Functionality, and Chemical Modulation. Chemical Reviews. 111(10). 6064–6119. 284 indexed citations
15.
Lavieri, Robert R., Sarah Scott, Kwangho Kim, et al.. (2010). Design, Synthesis, and Biological Evaluation of Halogenated N-(2-(4-Oxo-1-phenyl-1,3,8-triazaspiro[4.5]decan-8-yl)ethyl)benzamides: Discovery of an Isoform-Selective Small Molecule Phospholipase D2 Inhibitor. Journal of Medicinal Chemistry. 53(18). 6706–6719. 75 indexed citations
16.
Lewis, Jana A., Sarah Scott, Robert R. Lavieri, et al.. (2009). Design and synthesis of isoform-selective phospholipase D (PLD) inhibitors. Part I: Impact of alternative halogenated privileged structures for PLD1 specificity. Bioorganic & Medicinal Chemistry Letters. 19(7). 1916–1920. 84 indexed citations
17.
Lavieri, Robert R., Sarah Scott, Jana A. Lewis, et al.. (2009). Design and synthesis of isoform-selective phospholipase D (PLD) inhibitors. Part II. Identification of the 1,3,8-triazaspiro[4,5]decan-4-one privileged structure that engenders PLD2 selectivity. Bioorganic & Medicinal Chemistry Letters. 19(8). 2240–2243. 56 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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