Jed Zaretzki

989 total citations
12 papers, 753 citations indexed

About

Jed Zaretzki is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pharmacology. According to data from OpenAlex, Jed Zaretzki has authored 12 papers receiving a total of 753 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 9 papers in Computational Theory and Mathematics and 9 papers in Pharmacology. Recurrent topics in Jed Zaretzki's work include Pharmacogenetics and Drug Metabolism (9 papers), Computational Drug Discovery Methods (9 papers) and Metabolomics and Mass Spectrometry Studies (7 papers). Jed Zaretzki is often cited by papers focused on Pharmacogenetics and Drug Metabolism (9 papers), Computational Drug Discovery Methods (9 papers) and Metabolomics and Mass Spectrometry Studies (7 papers). Jed Zaretzki collaborates with scholars based in United States and Denmark. Jed Zaretzki's co-authors include Curt M. Breneman, S. Joshua Swamidass, Patrik Rydberg, Matthew K. Matlock, Lars Olsen, Kristin P. Bennett, David E. Gloriam, Charles Bergeron, Gerald Moore and Tyler B. Hughes and has published in prestigious journals such as Bioinformatics, IEEE Transactions on Pattern Analysis and Machine Intelligence and Journal of Chemical Information and Modeling.

In The Last Decade

Jed Zaretzki

12 papers receiving 733 citations

Peers

Jed Zaretzki

CTM

MB

PHARM

SPECT

OC

Nikolay Savchuk United States

X. Chen Singapore

Yanli Wang China

Qian Xie United States

Oliver Horlacher Switzerland

Alexios Koutsoukas United Kingdom

Zhi Cao China

Markus Wagener Germany

Kevin P. Cross United States

Nikolay Savchuk United States

Jed Zaretzki

569 ×1.2

CTM

460 ×1.1

MB

132 ×0.4

PHARM

199 ×1.8

SPECT

152 ×1.8

OC

Citations per year, relative to Jed Zaretzki Jed Zaretzki (= 1×) peers Nikolay Savchuk

Countries citing papers authored by Jed Zaretzki

Since Specialization

Citations

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

Fields of papers citing papers by Jed Zaretzki

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jed Zaretzki

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

All Works

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12 of 12 papers shown

1.

Zaretzki, Jed, et al.. (2015). Extending P450 site-of-metabolism models with region-resolution data. Bioinformatics. 31(12). 1966–1973. 13 indexed citations

2.

Zaretzki, Jed, et al.. (2015). Improved Prediction of CYP-Mediated Metabolism with Chemical Fingerprints. Journal of Chemical Information and Modeling. 55(5). 972–982. 12 indexed citations

3.

Zaretzki, Jed, Matthew K. Matlock, & S. Joshua Swamidass. (2013). XenoSite: Accurately Predicting CYP-Mediated Sites of Metabolism with Neural Networks. Journal of Chemical Information and Modeling. 53(12). 3373–3383. 175 indexed citations

4.

Zaretzki, Jed, et al.. (2013). DR-Predictor: Incorporating Flexible Docking with Specialized Electronic Reactivity and Machine Learning Techniques to Predict CYP-Mediated Sites of Metabolism. Journal of Chemical Information and Modeling. 53(12). 3352–3366. 26 indexed citations

5.

Matlock, Matthew K., Jed Zaretzki, & S. Joshua Swamidass. (2013). Scaffold network generator: a tool for mining molecular structures. Bioinformatics. 29(20). 2655–2656. 16 indexed citations

6.

Zaretzki, Jed, Patrik Rydberg, Charles Bergeron, et al.. (2012). RS-Predictor Models Augmented with SMARTCyp Reactivities: Robust Metabolic Regioselectivity Predictions for Nine CYP Isozymes. Journal of Chemical Information and Modeling. 52(6). 1637–1659. 68 indexed citations

7.

Zaretzki, Jed, et al.. (2012). RS-WebPredictor: a server for predicting CYP-mediated sites of metabolism on drug-like molecules. Bioinformatics. 29(4). 497–498. 49 indexed citations

8.

Moore, Gerald, et al.. (2011). Fast Bundle Algorithm for Multiple-Instance Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 34(6). 1068–1079. 57 indexed citations

9.

Zaretzki, Jed, et al.. (2011). RS-Predictor: A New Tool for Predicting Sites of Cytochrome P450-Mediated Metabolism Applied to CYP 3A4. Journal of Chemical Information and Modeling. 51(7). 1667–1689. 76 indexed citations

10.

Rydberg, Patrik, David E. Gloriam, Jed Zaretzki, Curt M. Breneman, & Lars Olsen. (2010). SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism. ACS Medicinal Chemistry Letters. 1(3). 96–100. 223 indexed citations

11.

Zaretzki, Jed, et al.. (2009). A novel method for predicting ligand regioselectivity to metabolism by the CYP3A4 enzyme. Chemistry Central Journal. 3(S1). 10 indexed citations

12.

Bergeron, Charles, Jed Zaretzki, Curt M. Breneman, & Kristin P. Bennett. (2008). Multiple instance ranking. 48–55. 28 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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