ToxBERT: an explainable AI framework for enhancing prediction of adverse drug reactions and structural insights.

ABSTRACT: Accurate prediction of drug-induced adverse drug reactions (ADRs) is crucial for drug safety evaluation, as it directly impacts public health and safety. While various models have shown promising results in predicting ADRs, their accuracy still needs improvement. Additionally, many existing models often lack interpretability when linking molecular structures to specific ADRs and frequently rely on manually selected molecular fingerprints, which can introduce bias. To address these challenges, we propose ToxBERT, an efficient transformer encoder model that leverages attention and masking mechanisms for simplified molecular input line entry system (SMILES) representations. Our results demonstrate that ToxBERT achieved area under the receiver operating characteristic curve (AUROC) scores of 0

SUBMITTER: He Y 

PROVIDER: S-EPMC12446765 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature