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<p>Lead optimization to improve pharmacokinetics and toxicity while maintaining biological activity is an important and costly stage in the drug discovery process, requiring computational approaches for increased efficiency. We propose GraphBioisostere, a bioisostere prediction model that uses graph neural networks. The proposed model leverages a large-scale matched molecular pair dataset constructed from the ChEMBL database and directly learns bioisosterism without target information by considering entire chemical structures. Our evaluation shows that incorporating whole-molecule context improves bioisostere prediction compared to fragment/substituent-only inputs. Compared with a strong fingerprint-based LightGBM baseline, GraphBioisostere achieves competitive prediction performance, with the best GNN variant approaching the baseline ROC-AUC. Additionally, models pre-trained on target-independent bioisostere prediction improved transfer learning performance for potency change prediction against specific targets, particularly in low-data settings. This suggests that GraphBioisostere acquires useful representations of the relationship between chemical structure and activity. Our research provides a tool to evaluate the potential of structural changes in molecular pairs to maintain activity independently of targets, contributing to improved efficiency in the drug discovery process.</p>

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GraphBioisostere: general bioisostere prediction model with deep graph neural network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月6日

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  • Sho Masunaga,
  • Kairi Furui,
  • Apakorn Kengkanna,
  • Masahito Ohue

摘要

Lead optimization to improve pharmacokinetics and toxicity while maintaining biological activity is an important and costly stage in the drug discovery process, requiring computational approaches for increased efficiency. We propose GraphBioisostere, a bioisostere prediction model that uses graph neural networks. The proposed model leverages a large-scale matched molecular pair dataset constructed from the ChEMBL database and directly learns bioisosterism without target information by considering entire chemical structures. Our evaluation shows that incorporating whole-molecule context improves bioisostere prediction compared to fragment/substituent-only inputs. Compared with a strong fingerprint-based LightGBM baseline, GraphBioisostere achieves competitive prediction performance, with the best GNN variant approaching the baseline ROC-AUC. Additionally, models pre-trained on target-independent bioisostere prediction improved transfer learning performance for potency change prediction against specific targets, particularly in low-data settings. This suggests that GraphBioisostere acquires useful representations of the relationship between chemical structure and activity. Our research provides a tool to evaluate the potential of structural changes in molecular pairs to maintain activity independently of targets, contributing to improved efficiency in the drug discovery process.