William Gowen-MacDonald

480 total citations · 1 hit paper
6 papers, 381 citations indexed

About

William Gowen-MacDonald is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cellular and Molecular Neuroscience. According to data from OpenAlex, William Gowen-MacDonald has authored 6 papers receiving a total of 381 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 2 papers in Computational Theory and Mathematics and 1 paper in Cellular and Molecular Neuroscience. Recurrent topics in William Gowen-MacDonald's work include Receptor Mechanisms and Signaling (3 papers), Computational Drug Discovery Methods (2 papers) and Chemical Synthesis and Analysis (1 paper). William Gowen-MacDonald is often cited by papers focused on Receptor Mechanisms and Signaling (3 papers), Computational Drug Discovery Methods (2 papers) and Chemical Synthesis and Analysis (1 paper). William Gowen-MacDonald collaborates with scholars based in United States and Italy. William Gowen-MacDonald's co-authors include David H. Rominger, Jonathan D. Violin, Robert J. Lefkowitz, Scott M. DeWire, Seungkirl Ahn, Sudarshan Rajagopal, Conrad L. Cowan, Aimee L. Crombie, Edward R. Siuda and Michael Koblish and has published in prestigious journals such as Cancer Research, Journal of Pharmacology and Experimental Therapeutics and Molecular Pharmacology.

In The Last Decade

William Gowen-MacDonald

6 papers receiving 373 citations

Hit Papers

align trajectories

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.

Quantifying Ligand Bias at Seven-Transmembrane Receptors

2011 306 citations Sudarshan Rajagopal, Seungkirl Ahn et al. Molecular Pharmacology profile →

Peers

William Gowen-MacDonald

MB

CMN

CTM

RNMI

PHYSI

Rory Sleno Canada

Wanjing Guo China

Ravi Kumar Verma Singapore

Ee Von Moo Denmark

Brian J. Holleran Canada

Yangxia Tan China

Yanting Yin United States

Sara Marsango United Kingdom

Nicole A. Perry United States

Takanobu Takezako Japan

Rory Sleno Canada

William Gowen-MacDonald

424 ×1.2

MB

247 ×1.1

CMN

28 ×0.5

CTM

52 ×1.6

RNMI

29 ×0.9

PHYSI

Citations per year, relative to William Gowen-MacDonald William Gowen-MacDonald (= 1×) peers Rory Sleno

Countries citing papers authored by William Gowen-MacDonald

Since Specialization

Citations

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

Fields of papers citing papers by William Gowen-MacDonald

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William Gowen-MacDonald. 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 William Gowen-MacDonald. The network helps show where William Gowen-MacDonald may publish in the future.

Co-authorship network of co-authors of William Gowen-MacDonald

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

All Works

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6 of 6 papers shown

1.

Bhagwat, Neha, William Gowen-MacDonald, Min Wang, et al.. (2021). Abstract 983: Preclinical characterization of PRT1419, a potent, selective and orally available inhibitor of MCL1. Cancer Research. 81(13_Supplement). 983–983. 15 indexed citations

2.

Zhang, Yang W., Liang Lü, Min Wang, et al.. (2021). Abstract P237: PRT2527 is a potent and selective CDK9 inhibitor that demonstrates anti-cancer activity in preclinical models of hematological malignancies and solid tumors with MYC amplification. Molecular Cancer Therapeutics. 20(12_Supplement). P237–P237. 3 indexed citations

3.

Bhagwat, Neha, Yang Zhang, Hong Lin, et al.. (2020). Abstract 2915: Preclinical characterization of PRT543, a potent and selective inhibitor of protein arginine methyltransferase 5 (PRMT5), with broad antitumor activity in in vitro and in vivo models. Cancer Research. 80(16_Supplement). 2915–2915. 5 indexed citations

4.

Koblish, Michael, Richard W. Carr, Edward R. Siuda, et al.. (2017). TRV0109101, a G Protein-Biased Agonist of the µ-Opioid Receptor, Does Not Promote Opioid-Induced Mechanical Allodynia following Chronic Administration. Journal of Pharmacology and Experimental Therapeutics. 362(2). 254–262. 27 indexed citations

5.

Rominger, David H., Conrad L. Cowan, William Gowen-MacDonald, & Jonathan D. Violin. (2014). Biased ligands: pathway validation for novel GPCR therapeutics. Current Opinion in Pharmacology. 16. 108–115. 25 indexed citations

6.

Rajagopal, Sudarshan, Seungkirl Ahn, David H. Rominger, et al.. (2011). Quantifying Ligand Bias at Seven-Transmembrane Receptors. Molecular Pharmacology. 80(3). 367–377. 306 indexed citations breakdown →

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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