HyperPhS:通过对比的超图学习来预测代谢稳定性的药物导向多式模式表示框架.
Xiaoyi Liu1, Na Zhang2, Chenglong Kang2
1School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing 100029, China.
Bioinformatics (Oxford, England)
|September 22, 2025
概括
一个新的框架,HyperPhS,使用药组和超图表表示来预测药物的代谢稳定性. 该工具通过识别关键功能组和改进化合物优化来增强药物发现.
科学领域:
- 药物发现和开发 药物发现和开发
- 计算化学的计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 代谢稳定性对于候选药物的查和优化至关重要.
- 功能组 (药理组) 影响药物代谢,但预测这种影响是具有挑战性的.
研究的目的:
- 开发一种准确和可解释的方法,以基于药组来预测代谢稳定性.
- 通过识别关键功能组和优化化合物来简化药物发现.
主要方法:
- 提出了一种以法马科福为指导的超图表达框架 (HyperPhS).
- 利用多视图表示和对比学习来从代谢药中提取特征.
- 集成的多式联络表示使用注意力驱动的融合模块和ChatGPT.
主要成果:
- 在HLM数据集 (87.6%AUC,62.6%MCC) 和外部验证 (88.3%AUC) 上,HyperPhS实现了高性能.
- 通过案例研究证明了药组的可解释性.
- 确定了对代谢稳定性的关键功能组.
结论:
- HyperPhS是预测代谢稳定的有效和可解释的工具.
- 该框架有助于识别关键功能组和优化药物化合物.
- 促进有效的药物发现和开发过程.
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