Zina Itkin

1.7k total citations
24 papers, 466 citations indexed

About

Zina Itkin is a scholar working on Molecular Biology, Computational Theory and Mathematics and Infectious Diseases. According to data from OpenAlex, Zina Itkin has authored 24 papers receiving a total of 466 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 9 papers in Computational Theory and Mathematics and 8 papers in Infectious Diseases. Recurrent topics in Zina Itkin's work include Computational Drug Discovery Methods (9 papers), SARS-CoV-2 and COVID-19 Research (7 papers) and COVID-19 Clinical Research Studies (4 papers). Zina Itkin is often cited by papers focused on Computational Drug Discovery Methods (9 papers), SARS-CoV-2 and COVID-19 Research (7 papers) and COVID-19 Clinical Research Studies (4 papers). Zina Itkin collaborates with scholars based in United States, South Africa and China. Zina Itkin's co-authors include Richard T. Eastman, Paul Shinn, Alexey Zakharov, Matthew D. Hall, Kelli M. Wilson, Regina Barzilay, Tommi Jaakkola, Wengong Jin, Min Shen and Jonathan Stokes and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Molecular Therapy.

In The Last Decade

Zina Itkin

24 papers receiving 465 citations

Peers

Zina Itkin

CTM

EPIDE

Jae-June Dong South Korea

Anchala Kumari India

Nguyen Quoc Thai Vietnam

Carleen Klumpp‐Thomas United States

Yi-Yu Ke Taiwan

Yuko Kawasaki Japan

Rubén Gil‐Redondo Spain

Amr Alhossary Singapore

Surasak Chunsrivirot Thailand

Carmine Talarico Italy

Jae-June Dong South Korea

Zina Itkin

106 ×0.6

183 ×1.0

95 ×0.6

CTM

27 ×0.7

57 ×1.6

EPIDE

Citations per year, relative to Zina Itkin Zina Itkin (= 1×) peers Jae-June Dong

Countries citing papers authored by Zina Itkin

Since Specialization

Citations

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

Fields of papers citing papers by Zina Itkin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zina Itkin

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

All Works

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20 of 20 papers shown

Ellison, S. Tori, Zina Itkin, Min Shen, et al.. (2025). Limitations of acyclovir and identification of potent HSV antivirals using 3D bioprinted human skin equivalents. Nature Communications. 16(1). 9200–9200. 1 indexed citations

Pourmousa, Mohsen, Sankalp Jain, Elena Barnaeva, et al.. (2025). AI-driven discovery of synergistic drug combinations against pancreatic cancer. Nature Communications. 16(1). 4020–4020. 6 indexed citations

Hu, Xin, Paul Shinn, Zina Itkin, et al.. (2024). A Comprehensive Collection of Pain and Opioid Use Disorder Compounds for High-Throughput Screening and Artificial Intelligence-Driven Drug Discovery. ACS Pharmacology & Translational Science. 7(8). 2391–2400. 2 indexed citations

Itkin, Zina, Malkhey Verma, John Janiszewski, et al.. (2024). High-resolution acoustic ejection mass spectrometry for high-throughput library screening. SLAS TECHNOLOGY. 29(6). 100199–100199. 4 indexed citations

Ceribelli, Michele, Christopher Melani, Mark Roschewski, et al.. (2024). Multi-Component, Time-Course screening to develop combination cancer therapies based on synergistic toxicity. Proceedings of the National Academy of Sciences. 121(49). e2413372121–e2413372121. 2 indexed citations

Chory, Emma J., Meng Wang, Michele Ceribelli, et al.. (2023). High-throughput approaches to uncover synergistic drug combinations in leukemia. SLAS DISCOVERY. 28(4). 193–201. 4 indexed citations

Shrimp, Jonathan H., John Janiszewski, Catherine Z. Chen, et al.. (2022). Suite of TMPRSS2 Assays for Screening Drug Repurposing Candidates as Potential Treatments of COVID-19. ACS Infectious Diseases. 8(6). 1191–1203. 6 indexed citations

Ronzetti, Michael, Bolormaa Baljinnyam, Zina Itkin, et al.. (2022). Application of temperature-responsive HIS-tag fluorophores to differential scanning fluorimetry screening of small molecule libraries. Frontiers in Pharmacology. 13. 1040039–1040039. 3 indexed citations

Boutin, Molly E., Caroline E. Strong, Xin Hu, et al.. (2022). A multiparametric calcium signal screening platform using iPSC-derived cortical neural spheroids.. SLAS DISCOVERY. 27(4). 209–218. 11 indexed citations

10.

Jin, Wengong, Jonathan Stokes, Richard T. Eastman, et al.. (2021). Deep learning identifies synergistic drug combinations for treating COVID-19. Proceedings of the National Academy of Sciences. 118(39). 114 indexed citations

11.

Hu, Xin, Catherine Z. Chen, Miao Xu, et al.. (2021). Discovery of Small Molecule Entry Inhibitors Targeting the Fusion Peptide of SARS-CoV-2 Spike Protein. ACS Medicinal Chemistry Letters. 12(8). 1267–1274. 16 indexed citations

12.

Ryu, Seungmi, Pei‐Hsuan Chu, Claire Malley, et al.. (2021). Human Pluripotent Stem Cells for High-Throughput Drug Screening and Characterization of Small Molecules. Methods in molecular biology. 2454. 811–827. 5 indexed citations

13.

Chen, Catherine Z., Paul Shinn, Zina Itkin, et al.. (2021). Drug Repurposing Screen for Compounds Inhibiting the Cytopathic Effect of SARS-CoV-2. Frontiers in Pharmacology. 11. 592737–592737. 61 indexed citations

14.

Itkin, Zina, Lu Chen, Gergely Zahoránszky-Köhalmi, et al.. (2020). Modulation of Triple Artemisinin-Based Combination Therapy Pharmacodynamics by Plasmodium falciparum Genotype. ACS Pharmacology & Translational Science. 3(6). 1144–1157. 7 indexed citations

15.

Hanson, Quinlin, Kelli M. Wilson, Min Shen, et al.. (2020). Targeting ACE2–RBD Interaction as a Platform for COVID-19 Therapeutics: Development and Drug-Repurposing Screen of an AlphaLISA Proximity Assay. ACS Pharmacology & Translational Science. 3(6). 1352–1360. 63 indexed citations

16.

Bobrowski, Tesia, Lu Chen, Richard T. Eastman, et al.. (2020). Synergistic and Antagonistic Drug Combinations against SARS-CoV-2. Molecular Therapy. 29(2). 873–885. 77 indexed citations

17.

Shinn, Paul, Lu Chen, Marc Ferrer, et al.. (2019). High-Throughput Screening for Drug Combinations. Methods in molecular biology. 1939. 11–35. 15 indexed citations

18.

Tong, Zhi‐Bin, Ruili Huang, Yuhong Wang, et al.. (2017). The Toxmatrix: Chemo-Genomic Profiling Identifies Interactions That Reveal Mechanisms of Toxicity. Chemical Research in Toxicology. 31(2). 127–136. 9 indexed citations

19.

Smith, Thomas M., et al.. (2012). Comparison of Compound Administration Methods in Biochemical Assays: Effects on Apparent Compound Potency Using Either Assay-Ready Compound Plates or Pin Tool–Delivered Compounds. SLAS DISCOVERY. 18(1). 14–25. 10 indexed citations

20.

Darrow, Andrew L., Kelly A. Conway, Anil H. Vaidya, et al.. (2003). Virus-Based Expression Systems Facilitate Rapid Target In Vivo Functionality Validation and High-Throughput Screening. SLAS DISCOVERY. 8(1). 65–71. 8 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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