  Adverse drug events (ADEs) significantly impact clinical research, causing
many clinical trial failures. ADE prediction is key for developing safer
medications and enhancing patient outcomes. To support this effort, we
introduce CT-ADE, a dataset for multilabel predictive modeling of ADEs in
monopharmacy treatments. CT-ADE integrates data from 2,497 unique drugs,
encompassing 168,984 drug-ADE pairs extracted from clinical trials, annotated
with patient and contextual information, and comprehensive ADE concepts
standardized across multiple levels of the MedDRA ontology. Preliminary
analyses with large language models (LLMs) achieved F1-scores up to 55.90%.
Models using patient and contextual information showed F1-score improvements of
21%-38% over models using only chemical structure data. Our results highlight
the importance of target population and treatment regimens in the predictive
modeling of ADEs, offering greater performance gains than LLM domain
specialization and scaling. CT-ADE provides an essential tool for researchers
aiming to leverage artificial intelligence and machine learning to enhance
patient safety and minimize the impact of ADEs on pharmaceutical research and
development. The dataset is publicly accessible at
https://github.com/ds4dh/CT-ADE.
