Evaluation of partitioning algorithms for trustworthy out-of-distribution evaluation of machine learning models in biochemistryRaúl Fernández DíazLam Thanh Hoanget al.2025VIBE 2025
How to build machine learning models able to extrapolate from standard to modified peptidesRaúl Fernández DíazRodrigo Ochoaet al.2025Journal of Cheminformatics
How to generalize machine learning models to both canonical and non-canonical peptidesRaúl Fernández DíazRodrigo Ochoaet al.2025ACS Fall 2025
Triple activation of germinated seeds diversifies protein breakdownIndrani BeraRaúl Fernández Díazet al.2025ACS Fall 2025
AutoPeptideML 2: An open source library for democratizing machine learning for peptide bioactivity predictionRaúl Fernández DíazLam Thanh Hoanget al.2025ISMB 2025
A new framework for evaluating model out-of-distribution generalisation for the biochemical domainRaúl Fernández DíazLam Thanh Hoanget al.2025ICLR 2025
Molecular Modelling in Bioactive Peptide Discovery and CharacterisationClement AgoniRaúl Fernández Díazet al.2025Molecules
A new framework for evaluating machine learning in biochemistry and its application for small molecules and peptidesRaúl Fernández DíazLam Thanh Hoanget al.2025IRB-AI-DD 2025
Effect of dataset partitioning strategies for evaluating out-of-distribution generalisation for predictive models in biochemistryRaúl Fernández DíazLam Thanh Hoanget al.2024ACS Fall 2024
Analysis of docking for binding affinity predictionRaúl Fernández DíazDenis Shieldset al.2024ACS Fall 2024