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<p>Drug-induced liver injury (DILI) presents a significant challenge due to its complexity, small datasets, and severe class imbalance. While unsupervised pretraining is a common approach to learn molecular representations for downstream tasks, it often lacks insights into how molecules interact with biological systems. We therefore introduce VitroBERT, a bidirectional encoder representations from transformers (BERT) model pretrained on large-scale in vitro assay profiles to generate biologically informed molecular embeddings. When leveraged to predict in vivo DILI endpoints, these embeddings delivered up to a 29% improvement in biochemistry-related tasks and a 16% gain in histopathology endpoints compared to unsupervised pretraining (MolBERT). However, no significant improvement was observed in clinical tasks. Furthermore, to address the critical issue of class imbalance, we evaluated multiple loss functions-including BCE, weighted BCE, Focal loss, and weighted Focal loss-and identified weighted Focal loss as the most effective. Our findings demonstrate the potential of integrating biological context into molecular models and highlight the importance of selecting appropriate loss functions in improving model performance of highly imbalanced DILI-related tasks. </p>

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VitroBert: modeling DILI by pretraining BERT on in vitro data

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月6日

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  • Muhammad Arslan Masood,
  • Anamya Ajjolli Nagaraja,
  • Katia Belaid,
  • Natalie Mesens,
  • Hugo Ceulemans,
  • Samuel Kaski,
  • Dorota Herman,
  • Markus Heinonen

摘要

Drug-induced liver injury (DILI) presents a significant challenge due to its complexity, small datasets, and severe class imbalance. While unsupervised pretraining is a common approach to learn molecular representations for downstream tasks, it often lacks insights into how molecules interact with biological systems. We therefore introduce VitroBERT, a bidirectional encoder representations from transformers (BERT) model pretrained on large-scale in vitro assay profiles to generate biologically informed molecular embeddings. When leveraged to predict in vivo DILI endpoints, these embeddings delivered up to a 29% improvement in biochemistry-related tasks and a 16% gain in histopathology endpoints compared to unsupervised pretraining (MolBERT). However, no significant improvement was observed in clinical tasks. Furthermore, to address the critical issue of class imbalance, we evaluated multiple loss functions-including BCE, weighted BCE, Focal loss, and weighted Focal loss-and identified weighted Focal loss as the most effective. Our findings demonstrate the potential of integrating biological context into molecular models and highlight the importance of selecting appropriate loss functions in improving model performance of highly imbalanced DILI-related tasks.