Abstract Background: The Beers Criteria are widely used to identify potentially inappropriate medicines (PIMs) in older adults. However, it does not account for pharmacogenomic variability, which can lead to adverse drug reactions (ADRs) in genetically susceptible individuals. This study aimed to develop and validate a machine learning–driven framework that integrates pharmacogenomic data into the Beers Criteria to support personalized prescribing and reduce ADR risk. Methods: We designed a hybrid neural network combining graph attention networks (GATs) and Transformers to predict clinically relevant drug–gene associations using multi-omics, pharmacogenomic and ADR datasets. In silico quantitative trait locus mapping was performed to validate predicted interactions, complemented by experimental validation using the luciferase reporter assays. Pharmacogenomic interactions confirmed through this process were proposed as contraindications for integration into the Beers Criteria. The framework was implemented using PyTorch Geometric and the Hugging Face Transformer library. Results: The GAT-Transformer model achieved high predictive performance, with an area under the receiver operating characteristic curve of 0.92, area under the precision-recall curve of 0.87 and F1 score of 0.83 for 78 high-risk Beer’s medications, outperforming baseline models. Amongst 14,520 candidate drug–gene pairs, 327 novel interactions were predicted (false discovery rate < 0.05) and 31 were experimentally validated, yielding a validation rate of 73.8%. Conclusion: This framework provides a scalable, evidence-based approach to integrate pharmacogenomics into PIM assessment, enhancing the Beers Criteria for genetically susceptible elderly populations. By identifying patient-specific risk factors for ADRs, it has the potential to improve medication safety and optimise geriatric pharmacotherapy outcomes.
Al-Harthy et al. (2026) studied this question.