Leandro A. Bugnon

> AI x BIO

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My research goal is to develop AI methods that decode the language of biology, enabling faster drug discovery, better disease diagnostics, and deeper understanding of molecular mechanisms.

I work at the intersection of AI and bioinformatics, with a focus on learning representations of biological sequences, RNA structure prediction, and conditional generative models.

I am an Associate Researcher at sinc(i)/UNL – CONICET, and an Associate Professor at the National University of the Litoral and Austral University. I also collaborate with The Cell Company on applied bioinformatics projects.

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2026.04.20 BioComputingUP @ Università di Padova (IDPfun2) · linker characterization 2025.12.15 Profesor Adjunto · UNL 2025.12.01 Associate Researcher · CONICET 2025.10.02 ANCEFN incentive · Engineering, <40 2025.04.01 B. Breggia + G. Kulemayer · sequence representations, joint seq/structure generative models
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  1. 2026
    ET-Pfam: ensemble transfer learning for protein family prediction
    Duarte, Vitale, Escudero et al. · Bioinformatics 42(4)
  2. 2026
    GNN2Pfam: Integrating protein sequence and structure with graph neural networks for Pfam domain annotation
    Fenoy, Bugnon, Vitale et al. · Journal of Structural Biology 218(1)
  3. 2025
    Big team science reveals promises and limitations of machine learning efforts to model physiological markers of affective experience
    Coles, Perz, Behnke et al. · Royal Society Open Science 12(6)
  4. 2025
    Comprehensive benchmarking of large language models for RNA secondary structure prediction
    Zablocki, Bugnon, Gerard et al. · Briefings in Bioinformatics 26(2)
  5. 2024
    sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure
    Bugnon, Di Persia, Gerard et al. · Briefings in Bioinformatics 25(4)