Digital Twin Approaches for Interpretable Side Effect Prediction in Drug Discovery
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