Raúl Fernández-Díaz

University College Dublin — Ireland

Biochemistry, Genetics and Molecular Biology · Life Sciences

5h-index52citations20works2.52yr avg
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Research Topics

Machine Learning in Bioinformatics(8), Computational Drug Discovery Methods(5), Advanced Proteomics Techniques and Applications(5), Chemical Synthesis and Analysis(4), vaccines and immunoinformatics approaches(4)

Publications20 total

Peptidomics Mapping of Proteolysis Highlights Triple Activation of Sprouted Seeds by Germination, Homogenisation and Species Mixture
2026Open Access
How to build machine learning models able to extrapolate from standard to modified peptides
Journal of Cheminformatics·2025Open Access
How to build machine learning models able to extrapolate from standard to modified peptides
2025· 1 cited
How to build machine learning models able to extrapolate from standard to modified peptides
2025Open Access
BioBrigit, a Hybrid Machine Learning and Knowledge-Based Approach to Model Metal Pathways in Proteins: Application to a Dicopper Tyrosinase
ACS Omega·2025· 2 citedOpen Access
Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)
ArXiv.org·2025· 2 citedOpen Access
How to generalize machine learning models to both canonical and non-canonical peptides
2025Open Access
HemeFinder: a Computational Predictor for Heme-Binding Sites in Proteins
2025Open Access
Molecular Modelling in Bioactive Peptide Discovery and Characterisation
Biomolecules·2025· 13 citedOpen Access
How to generalize machine learning models to both canonical and non-canonical peptides
2025· 1 citedOpen Access
How to build machine learning models able to extrapolate from standard to modified peptides
Figshare·2025Open Access
Additional file 1 of How to build machine learning models able to extrapolate from standard to modified peptides
Figshare·2025Open Access
Additional file 1 of How to build machine learning models able to extrapolate from standard to modified peptides
Figshare·2025Open Access
How to build machine learning models able to extrapolate from standard to modified peptides
Figshare·2025Open Access
Enhancing foundation models for scientific discovery via multimodal knowledge graph representations
Journal of Web Semantics·2024· 8 citedOpen Access
AutoPeptideML: a study on how to build more trustworthy peptide bioactivity predictors
Bioinformatics·2024· 9 citedOpen Access
Knowledge Enhanced Representation Learning for Drug Discovery
Proceedings of the AAAI Conference on Artificial Intelligence·2024· 5 citedOpen Access
A new framework for evaluating model out-of-distribution generalisation for the biochemical domain
2024· 4 citedOpen Access
AutoPeptideML: A study on how to build more trustworthy peptide bioactivity predictors
2023· 5 citedOpen Access
Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery
arXiv (Cornell University)·2023· 2 citedOpen Access

Frequent Co-authors

Vanessa López(11), Thanh Lam Hoang(11), Denis C. Shields(10), Rodrigo Ochoa(5), Rodrigo Ochoa(4), Vanessa Lopez(4), Denis C. Shields(4), Clement Agoni(3), Hoang Thanh Lam(3), Marco Luca Sbodio(3), Mykhaylo Zayats(3), Rodrigo Cossio-Pérez(2), Víctor Valls(2), Gabriele Picco(2), Jean‐Didier Maréchal(2), Patrick Brendan Timmons(1), Alessandro Adelfio(1), Hansel Gómez(1), Lam Thanh Hoang(1), Marcos Martinez-Galindo(1)