Uxía Veleiro is a Postdoctoral Associate in the Integrative Computational Network Biology Lab at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), under the supervision of Professor Nataša Pržulj. She specializes in machine learning and bioinformatics, with a particular focus on graph-based deep learning methods applied to drug repurposing and patient-aware therapeutic discovery.
Dr. Veleiro’s research centers on designing novel ML models for drug repurposing, multi-omics data fusion, and network science applied to precision medicine and precision therapeutics. Her recent work tackles fundamental challenges in drug-target interaction prediction, including the development of inductive learning approaches that generalize beyond training data. Her methods have been published in top-tier journals including Nature Machine Intelligence (IF 18.8), Oxford Bioinformatics (IF 4.4), and Computational and Structural Biotechnology Journal (IF 6.0). Her work on inductive drug repurposing was selected for an oral presentation at the AI4D3 workshop at NeurIPS 2023 (acceptance ratio 6/76).
Before joining MBZUAI, Dr. Veleiro completed her doctoral research at CIMA University of Navarra (2022-2026), where she defended her PhD thesis with cum laude distinction and an international doctorate mention, under the supervision of Professors Mikel Hernaez and Antonio Pineda-Lucena. During her PhD, she was a Visiting Researcher at the University of Basel under Professor Ivan Dokmanić (2024-2025), supported by a competitive PhD mobility grant from the Government of Navarre. She has developed two open-source software packages: GeNNius, an ultrafast drug-target interaction inference method based on graph neural networks, and GraphEmb, a benchmarking framework for drug-target interaction models. Dr. Veleiro has co-organized the Madrid Meet Up of the Learning on Graphs Conference (2023) and the PhD Day at CIMA (2024-2026), and was selected for the highly competitive Mediterranean Machine Learning Summer School (M2L 2025, 18% acceptance rate, with a travel grant) and the Oxford Machine Learning Summer School (OxML 2024).
Education
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PhD in Applied Medicine and Biomedicine, University of Navarra (UNAV), Pamplona, Spain (2022-2026)
- Thesis: “Drug repurposing through deep learning on graphs”
- Defended with cum laude distinction and international doctorate mention
- Supervisors: Professor Mikel Hernaez and Professor Antonio Pineda-Lucena
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MSc in Quantitative Biotechnology, University of Zaragoza, Spain (2020-2021)
- Master Thesis (30 ECTS): “BFT-3: Virtual Screening - Combining Experimental and Computational Tools”
- Focus: Molecular docking and molecular dynamics for protein-ligand interactions
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BSc in Physics, University of Santiago de Compostela (USC), Spain (2015-2020)
- Bachelor Thesis: “Cyclodextrin as drug carrier for Ibuprofen using Molecular Dynamics simulations”
- Erasmus exchange: Ruprecht-Karls-Universität Heidelberg, Germany (2018-2019)
Professional Positions
Current Appointments
- Postdoctoral Associate, Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE (2026 – present)
Previous Positions
- Doctoral Researcher, CIMA University of Navarra, Pamplona, Spain (2022 – 2026)
- Visiting Researcher, University of Basel, Switzerland (2024 – 2025)
- Research Intern, MD.USE Innovative Solutions, Galicia, Spain (2020)
Student Supervision
- MSc Thesis Student, University of Zaragoza
- Bachelor’s Thesis Student, Public University of Navarre
Teaching Experience
University of Navarra
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Sequence Analysis and Structural Bioinformatics (2021/22, 2022/23) [Teaching Assistant]
- MSc in Computational Methods
Selected Publications
- J. de la Fuente, G. Serrano, U. Veleiro, M. Casals, L. Vera, M. Pizurica, A. Pineda-Lucena, I. Ochoa, S. Vicent, O. Gevaert, M. Hernaez. “Towards a more inductive world for drug repurposing approaches.” *Nature Machine Intelligence (2025). DOI
- U. Veleiro, J. de la Fuente, G. Serrano, M. Pizurica, M. Casals, A. Pineda-Lucena, S. Vicent, I. Ochoa, O. Gevaert, M. Hernaez. “GeNNius: An ultrafast drug-target interaction inference method based on graph neural networks.” Bioinformatics (2024). DOI
- F. Suárez, M. Calvelo, G.F. Tolufashe, A. Muñoz, U. Veleiro, C. Porto, M. Bastos, A. Piñeiro, R. Garcia-Fandino. “SuPepMem: A database of innate immune system peptides and their cell membrane interactions.” Computational and Structural Biotechnology Journal (2022). DOI
- P. F. Garrido, M. Calvelo, A. Blanco-González, U. Veleiro, F. Suárez, D. Conde, A. Cabezón, Á. Piñeiro, R. Garcia-Fandiño. “The Lord of the NanoRings: Cyclodextrins and the battle against SARS-CoV-2.” International Journal of Pharmaceutics (2020). DOI
Selected Conference Presentations
- Talk, ISMB/ECCB 2025, Liverpool, UK — “Towards a more inductive world for drug repurposing approaches” (July 2025)
- Oral Presentation, AI4D3 Workshop, NeurIPS 2023 — “Towards a more inductive world for drug repurposing approaches” (acceptance ratio 6/76)
- Flashtalk and Poster, Mining and Learning with Graphs Workshop (MLG) @ ECML PKDD 2023, Turin, Italy — “GeNNius” (September 2023)
- Poster, ISMB/ECCB 2023, Lyon, France — “GeNNius” (July 2023)
Awards and Honors
- Travel Grant, Mediterranean Machine Learning Summer School (M2L 2025), Split, Croatia (18% acceptance rate)
- PhD Mobility Grant, Government of Navarre (2024) — for visiting research at the University of Basel
- PhD Thesis Distinction: Cum laude with international doctorate mention, University of Navarra (2026)
Service and Outreach
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Organizer, PhD Day at CIMA, Pamplona, Spain (2024-2026)
- Organizing committee member; responsibilities included fundraising, event organization, budget management, and chairing the event
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Organizer, Madrid Meet Up — Learning on Graphs Conference, Madrid, Spain (2023)
- Spanish branch of the Learning on Graphs conference, organized in collaboration with Antonio G. Marqués (URJC)
- Hosted speakers from 13 academic institutions across Europe and 2 industry speakers
- Event website
Search for Uxía Veleiro's papers on the Publications page