Brian Y. Feng

2.5k total citations
18 papers, 1.9k citations indexed

About

Brian Y. Feng is a scholar working on Molecular Biology, Computational Theory and Mathematics and Biomedical Engineering. According to data from OpenAlex, Brian Y. Feng has authored 18 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 6 papers in Computational Theory and Mathematics and 5 papers in Biomedical Engineering. Recurrent topics in Brian Y. Feng's work include Computational Drug Discovery Methods (6 papers), Innovative Microfluidic and Catalytic Techniques Innovation (5 papers) and Click Chemistry and Applications (2 papers). Brian Y. Feng is often cited by papers focused on Computational Drug Discovery Methods (6 papers), Innovative Microfluidic and Catalytic Techniques Innovation (5 papers) and Click Chemistry and Applications (2 papers). Brian Y. Feng collaborates with scholars based in United States, Switzerland and China. Brian Y. Feng's co-authors include Brian K. Shoichet, Susan L. McGovern, Brian T. Helfand, Thompson N. Doman, Anang A. Shelat, R. Kiplin Guy, Anton Simeonov, Ajit Jadhav, Kerim Babaoglu and James Inglese and has published in prestigious journals such as Nature Communications, Genes & Development and PLoS ONE.

In The Last Decade

Brian Y. Feng

17 papers receiving 1.9k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Brian Y. Feng United States 11 1.2k 599 322 212 186 18 1.9k
Adam Yasgar United States 25 1.4k 1.2× 540 0.9× 271 0.8× 179 0.8× 87 0.5× 51 2.4k
Hwangseo Park South Korea 30 1.4k 1.2× 472 0.8× 648 2.0× 241 1.1× 110 0.6× 117 2.6k
Jung‐Hsin Lin Taiwan 24 1.7k 1.4× 585 1.0× 284 0.9× 180 0.8× 63 0.3× 61 2.4k
Ivanov As Russia 22 944 0.8× 259 0.4× 172 0.5× 168 0.8× 235 1.3× 183 1.8k
Hui Sun Lee United States 22 1.6k 1.3× 322 0.5× 310 1.0× 81 0.4× 88 0.5× 43 2.3k
S. Barret Kalindjian United Kingdom 20 1.3k 1.1× 657 1.1× 765 2.4× 179 0.8× 79 0.4× 52 2.6k
David L. Pincus United States 10 2.0k 1.7× 496 0.8× 322 1.0× 212 1.0× 69 0.4× 11 3.0k
Dmitry Lupyan United States 12 1.9k 1.6× 723 1.2× 569 1.8× 218 1.0× 80 0.4× 14 3.0k
Peter Schmidtke Germany 18 2.3k 1.9× 1.1k 1.9× 282 0.9× 355 1.7× 108 0.6× 26 3.2k
Grant R. Zimmermann United States 13 2.0k 1.6× 684 1.1× 313 1.0× 285 1.3× 70 0.4× 15 3.0k

Countries citing papers authored by Brian Y. Feng

Since Specialization
Citations

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

Fields of papers citing papers by Brian Y. Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brian Y. Feng. 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 Brian Y. Feng. The network helps show where Brian Y. Feng may publish in the future.

Co-authorship network of co-authors of Brian Y. Feng

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

All Works

18 of 18 papers shown
1.
Feng, Brian Y., Yiqing Yang, Yuli Zhu, et al.. (2025). Molecular Effects of Zwitterionic Peptide on Monolayer Lipid Membranes upon Enzyme-Catalyzed Degradation. Langmuir. 41(5). 3402–3412. 1 indexed citations
2.
Olsson, Niclas, Austin E.Y.T. Lefebvre, Mark E. Fitzgerald, et al.. (2025). BiDAC-dependent degradation of plasma membrane proteins by the endolysosomal system. Nature Communications. 16(1). 4345–4345. 1 indexed citations
3.
Coram, Marc, Lisha Wang, William J. Godinez, et al.. (2021). Morphological Characterization of Antibiotic Combinations. ACS Infectious Diseases. 8(1). 66–77. 5 indexed citations
4.
Sawyer, William S., Lisha Wang, Tsuyoshi Uehara, et al.. (2019). Targeted lipopolysaccharide biosynthetic intermediate analysis with normal-phase liquid chromatography mass spectrometry. PLoS ONE. 14(2). e0211803–e0211803. 6 indexed citations
5.
Godinez, William J., Helen Chan, Imtiaz Hossain, et al.. (2019). Morphological Deconvolution of Beta-Lactam Polyspecificity in E. coli. ACS Chemical Biology. 14(6). 1217–1226. 19 indexed citations
6.
Spangler, Benjamin, Shengtian Yang, Christopher M. Rath, Folkert Reck, & Brian Y. Feng. (2019). A Unified Framework for the Incorporation of Bioorthogonal Compound Exposure Probes within Biological Compartments. ACS Chemical Biology. 14(4). 725–734. 9 indexed citations
7.
Spangler, Benjamin, Dustin Dovala, William S. Sawyer, et al.. (2018). Molecular Probes for the Determination of Subcellular Compound Exposure Profiles in Gram-Negative Bacteria. ACS Infectious Diseases. 4(9). 1355–1367. 13 indexed citations
8.
Rath, Christopher M., Bret M. Benton, Javier de Vicente, et al.. (2017). Optimization of CoaD Inhibitors against Gram-Negative Organisms through Targeted Metabolomics. ACS Infectious Diseases. 4(3). 391–402. 13 indexed citations
9.
Hillman, R. Tyler, Brian Y. Feng, Jun Ni, et al.. (2011). Neuropilins are positive regulators of Hedgehog signal transduction. Genes & Development. 25(22). 2333–2346. 61 indexed citations
10.
Feng, Brian Y., Brandon H. Toyama, Holger Wille, et al.. (2008). Small-molecule aggregates inhibit amyloid polymerization. Nature Chemical Biology. 4(3). 197–199. 216 indexed citations
11.
Babaoglu, Kerim, Anton Simeonov, John J. Irwin, et al.. (2008). Comprehensive Mechanistic Analysis of Hits from High-Throughput and Docking Screens against β-Lactamase. Journal of Medicinal Chemistry. 51(8). 2502–2511. 130 indexed citations
12.
Feng, Brian Y.. (2007). The Detection, Prevalence and Properties of Aggregate-Based Small Molecule Inhibition. eScholarship (California Digital Library).
13.
Feng, Brian Y., Anton Simeonov, Ajit Jadhav, et al.. (2007). A High-Throughput Screen for Aggregation-Based Inhibition in a Large Compound Library. Journal of Medicinal Chemistry. 50(10). 2385–2390. 277 indexed citations
14.
Feng, Brian Y. & Brian K. Shoichet. (2007). Synergy and Antagonism of Promiscuous Inhibition in Multiple-Compound Mixtures. Journal of Medicinal Chemistry. 50(12). 2930–2930. 1 indexed citations
15.
Feng, Brian Y. & Brian K. Shoichet. (2006). A detergent-based assay for the detection of promiscuous inhibitors. Nature Protocols. 1(2). 550–553. 346 indexed citations
16.
Feng, Brian Y. & Brian K. Shoichet. (2006). Synergy and Antagonism of Promiscuous Inhibition in Multiple-Compound Mixtures. Journal of Medicinal Chemistry. 49(7). 2151–2154. 60 indexed citations
17.
Feng, Brian Y., Anang A. Shelat, Thompson N. Doman, R. Kiplin Guy, & Brian K. Shoichet. (2005). High-throughput assays for promiscuous inhibitors. Nature Chemical Biology. 1(3). 146–148. 257 indexed citations
18.
McGovern, Susan L., Brian T. Helfand, Brian Y. Feng, & Brian K. Shoichet. (2003). A Specific Mechanism of Nonspecific Inhibition. Journal of Medicinal Chemistry. 46(20). 4265–4272. 473 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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