Leandro A. Bugnon

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, on representation learning for biological sequences, structure prediction, active learning for virtual screening and binding prediction, and conditional generative models for diverse molecules like RNA, proteis and glycans.

I am an Associate Researcher at sinc(i)/UNL – CONICET, where I also serve as deputy director, 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.

When I’m not training neural networks I’m usually proofing dough — some baking algorithms to.

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2026-06 [preprint] Preprint: how should we cross-validate RNA folding models?A critical review of cross-validation strategies for RNA secondary structure prediction, and the homology leakage that makes results look better than they are. 2026-04 [visit] BioComputingUP @ Università di Padova (IDPfun2)Research visit to the BioComputingUP group within the IDPfun2 project: computational characterisation of linkers in intrinsically disordered proteins. 2026-04 [paper] ET-Pfam out in BioinformaticsEnsembles of transfer-learning classifiers that cut the protein family prediction error to 7%. 2026-04 [preprint] emb2dis preprint: protein disorder predictionResNets, dilated convolutions and protein language models for intrinsic disorder prediction. Joint work with BioComputingUP (Padova). 2026-02 [paper] GNN2Pfam out in Journal of Structural BiologyGraph neural networks that combine protein sequence and structure for Pfam domain annotation.

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