Fabio Urbina

1.2k total citations · 1 hit paper
36 papers, 696 citations indexed

About

Fabio Urbina is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cell Biology. According to data from OpenAlex, Fabio Urbina has authored 36 papers receiving a total of 696 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 17 papers in Computational Theory and Mathematics and 8 papers in Cell Biology. Recurrent topics in Fabio Urbina's work include Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (8 papers) and Cellular transport and secretion (4 papers). Fabio Urbina is often cited by papers focused on Computational Drug Discovery Methods (17 papers), Machine Learning in Materials Science (8 papers) and Cellular transport and secretion (4 papers). Fabio Urbina collaborates with scholars based in United States, Switzerland and United Kingdom. Fabio Urbina's co-authors include Sean Ekins, Stephanie L. Gupton, Filippa Lentzos, Cédric Invernizzi, Thomas R. Lane, Ana C. Puhl, Shalini Menon, Kimberley M. Zorn, Shawn M. Gomez and Daniel H. Foil and has published in prestigious journals such as SHILAP Revista de lepidopterología, The Journal of Cell Biology and Environmental Science & Technology.

In The Last Decade

Fabio Urbina

35 papers receiving 674 citations

Hit Papers

Dual use of artificial-intelligence-powered drug discovery 2022 2026 2023 2024 2022 50 100 150
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.

  1. Dual use of artificial-intelligence-powered drug discovery
    2022 162 citations Fabio Urbina, Filippa Lentzos et al. Nature Machine Intelligence profile →

Peers

Fabio Urbina
Igor Filippov United States
Anastasiia Sadybekov United States
Seyed Shahriar Arab Iran
Jian Feng China
Sezen Vatansever United States
Mehdi Sadeghi Iran
Julia Koehler Leman United States
Edoardo Milanetti Italy
Marcin von Grotthuss United States
Xiao Gan United States
Igor Filippov United States
Fabio Urbina
Citations per year, relative to Fabio Urbina Fabio Urbina (= 1×) peers Igor Filippov

Countries citing papers authored by Fabio Urbina

Since Specialization
Citations

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

Fields of papers citing papers by Fabio Urbina

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabio Urbina

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

All Works

20 of 20 papers shown
1.
Burridge, Keith, Fabio Urbina, Stephanie L. Gupton, et al.. (2025). Vinculin and metavinculin exhibit distinct effects on focal adhesion properties, cell migration, and mechanotransduction. UNC Libraries. 1 indexed citations
2.
Lane, Thomas R., et al.. (2025). Computational Approaches for Predicting Drug Interactions with Human Organic Anion Transporter 4 (OAT4). Molecular Pharmaceutics. 22(4). 1847–1858. 2 indexed citations
3.
Lane, Thomas R., et al.. (2025). Machine Learning and Large Language Models for Modeling Complex Toxicity Pathways and Predicting Steroidogenesis. Environmental Science & Technology. 59(27). 13844–13856. 3 indexed citations
4.
Puhl, Ana C., et al.. (2024). The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications. Communications Chemistry. 7(1). 134–134. 13 indexed citations
5.
Lane, Thomas R., Vadim Makarov, Julie A. Nelson, et al.. (2023). N-Phenyl-1-(phenylsulfonyl)-1H-1,2,4-triazol-3-amine as a New Class of HIV-1 Non-nucleoside Reverse Transcriptase Inhibitor. Journal of Medicinal Chemistry. 66(9). 6193–6217. 6 indexed citations
6.
Lane, Thomas R., et al.. (2023). Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species. Chemical Research in Toxicology. 36(2). 188–201. 16 indexed citations
7.
Ekins, Sean, Thomas R. Lane, Fabio Urbina, & Ana C. Puhl. (2023). In silico ADME/tox comes of age: twenty years later. Xenobiotica. 54(7). 352–358. 7 indexed citations
8.
Ye, Michael, Michael Armstrong, Fabio Urbina, et al.. (2022). Coordinated Regulation of CB1 Cannabinoid Receptors and Anandamide Metabolism Stabilizes Network Activity during Homeostatic Downscaling. eNeuro. 9(6). ENEURO.0276–22.2022. 7 indexed citations
9.
Urbina, Fabio, Filippa Lentzos, Cédric Invernizzi, & Sean Ekins. (2022). Dual use of artificial-intelligence-powered drug discovery. Nature Machine Intelligence. 4(3). 189–191. 162 indexed citations breakdown →
10.
Urbina, Fabio & Sean Ekins. (2022). The commoditization of AI for molecule design. SHILAP Revista de lepidopterología. 2. 100031–100031. 6 indexed citations
11.
Blay, Vincent, et al.. (2022). Combining DELs and machine learning for toxicology prediction. Drug Discovery Today. 27(11). 103351–103351. 9 indexed citations
12.
Urbina, Fabio, Filippa Lentzos, Cédric Invernizzi, & Sean Ekins. (2022). A teachable moment for dual-use. Nature Machine Intelligence. 4(7). 607–607. 10 indexed citations
13.
Urbina, Fabio, Shalini Menon, Dennis Goldfarb, et al.. (2021). TRIM67 regulates exocytic mode and neuronal morphogenesis via SNAP47. Cell Reports. 34(6). 108743–108743. 15 indexed citations
14.
Urbina, Fabio & Stephanie L. Gupton. (2021). Automated Detection and Analysis of Exocytosis. Journal of Visualized Experiments. 5 indexed citations
15.
Urbina, Fabio, Ana C. Puhl, & Sean Ekins. (2021). Recent advances in drug repurposing using machine learning. Current Opinion in Chemical Biology. 65. 74–84. 45 indexed citations
16.
Urbina, Fabio & Stephanie L. Gupton. (2021). Automated Detection and Analysis of Exocytosis. Journal of Visualized Experiments.
17.
Urbina, Fabio & Stephanie L. Gupton. (2020). SNARE-Mediated Exocytosis in Neuronal Development. Frontiers in Molecular Neuroscience. 13. 133–133. 35 indexed citations
18.
Salani, Monica, Fabio Urbina, Elisabetta Morini, et al.. (2018). Development of a Screening Platform to Identify Small Molecules That Modify ELP1 Pre-mRNA Splicing in Familial Dysautonomia. SLAS DISCOVERY. 24(1). 57–67. 15 indexed citations
19.
Urbina, Fabio, Shawn M. Gomez, & Stephanie L. Gupton. (2018). Spatiotemporal organization of exocytosis emerges during neuronal shape change. The Journal of Cell Biology. 217(3). 1113–1128. 34 indexed citations
20.
Menon, Shalini, Cortney C. Winkle, Fabio Urbina, et al.. (2017). TRIM9-dependent ubiquitination of DCC constrains kinase signaling, exocytosis, and axon branching. Molecular Biology of the Cell. 28(18). 2374–2385. 35 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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