UGTformer:一种预训练的图形转换器模型,用于预测UDP-glucuronosyltransferase介导的药物代谢
Meiling Zhan1,2, Xiang Li1,2, Le Xiong1,2
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai, China.
Journal of cheminformatics
|February 4, 2026
概括
新的图形变压器模型UGTformer能够准确地预测药物代谢部位和基板. 该工具增强了对UDP-glucuronosyltransferases (UGTs) 代谢的理解,有助于药物开发.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学的计算化学
- 生物信息学是一种生物信息学.
背景情况:
- UDP-glucuronosyltransferases (UGTs) 通过葡萄糖化对药物代谢至关重要.
- 由于注释数据有限,预测UGT介导的代谢部位 (SOMs) 是一个挑战.
研究的目的:
- 介绍UGTformer,一个统一的图形变压器框架,用于同时进行UGT基质分类和SOM预测.
- 开发一种可靠和可扩展的方法来预测与UGT相关的新陈代谢.
主要方法:
- UGTformer使用具有多跳转消息传播和变压器编码器的层次架构.
- 该模型在分子图表上进行了预训练,使用自我监督的任务,并在精选的UGT代谢数据上进行了微调.
主要成果:
- 在交叉验证中,UGTformer的AUC为0.833的基质分类和0.884的SOM识别.
- 该模型在独立的外部验证集上显示出强大的概括性,超过了GNN基线.
结论:
- UGTformer提供了对UGT代谢的可解释和生物学一致的预测.
- 该框架提供了一个强大而可扩展的解决方案,用于预测UGT介导的药物代谢和代谢部位.
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