Pretrained transformer models for predicting the withdrawal of drugs from the market

Eyal Mazuz, Guy Shtar, Nir Kutsky, Lior Rokach, Bracha Shapira

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Motivation: The 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. Results: In this study, we employed pretrained large language models (LLMs) to predict the likelihood of a drug being withdrawn from the market due to safety concerns.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 Notably, our pretrained LLMs successfully identified over 50% drugs that were subsequently withdrawn, when predictions were made on a subset of drugs with inconsistent labeling between the training and test sets.

Original languageEnglish
Article numberbtad519
JournalBioinformatics
Volume39
Issue number8
DOIs
StatePublished - 1 Aug 2023

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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