Digital Twin Approaches for Interpretable Side Effect Prediction in Drug Discovery

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Authors: András Ecker, Erzsébet Fichó, Attila Csikász-Nagy
Cited by: 0

Abstract

Drug - side effect associations (preprocessed from OnSIDES 2.1.0) used for training side effect predictors, alongside data needed (and some more) to reproduce figures from the manuscript with the same title. Figure 2A: simulation_statistics.csvFigure 2B: protein_family_enrichment.csvFigure 2C: prediction_statistics.csvFigure 2D: side_effects_*.csv Figure 3B: interpretability_top10targets_Stevens-Johnson_syndrome.csvFigure 3C: interpretability_top10[complexes|pathways]_Alanine_aminotransferase_increased.csv and sign_perturbed_[complexes|pathways].csv Commercial re-use is not permitted, pls. see license and copyright!

Fields of Study & Subjects

Computational Drug Discovery Methods Pharmacovigilance and Adverse Drug Reactions Machine Learning in Healthcare

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
9 February 2026
Language
EN
Document type
Dataset