ArticleBioinformatics (Oxford, England)2023
Pretrained transformer models for predicting the withdrawal of drugs from the market.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Review
- A computationally efficient biomedical text processing framework for pharmacovigilance: integrating low-rank adaptation and interpretable AI for adverse drug reaction detection.Medical & biological engineering & computing · 2026Article
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Withdrawn 2.0-update on withdrawn drugs with pharmacovigilance data.Nucleic acids research · 2024Article
- Protein-ligand binding affinity prediction using multi-instance learning with docking structures.Frontiers in pharmacology · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationThe process of drug discovery is notoriously complex, costing an average of 2.6 billion dollars and taking ∼13 years to bring a new drug to the market. The success rate for new drugs is alarmingly low (around 0.0001%), and severe adverse drug reactions (ADRs) frequently occur, some of which may even result in death. Early identification of potential ADRs is critical to improve the efficiency and safety of the drug development process.
resultsIn this study, we employed pretrained large language models (LLMs) to predict the likelihood of a drug being withdrawn from the market due to safety concerns. Our method achieved an area under the curve (AUC) of over 0.75 through cross-database validation, outperforming classical machine learning models and graph-based models. Notably, our pretrained LLMs successfully identified over 50% drugs that were subsequently withdrawn, when predictions were made on a subset of drugs with inconsistent labeling between the training and test sets. AVAILABILITY AND IMPLEMENTATION: The code and datasets are available at https://github.com/eyalmazuz/DrugWithdrawn.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.