男人:利用可解释的多模式编码网络,精确预测CYP450抑制剂
Abena Achiaa Atwereboannah1,2, Wei-Ping Wu3,4, Mugahed A Al-Antari5
1School of Computer Science and Engineering, University of Electronic Science and Technology, Chengdu, People's Republic of China.
Scientific reports
|July 1, 2025
概括
一个新的多模式编码器网络 (MEN) 准确预测细胞P450 (CYP450) 酶抑制剂,提高药物安全性. 这种人工智能模型通过整合多样化的分子数据来提高药物开发,从而实现更好的预测.
科学领域:
- 计算化学和化学信息学
- 药理学和药物发现
- 医学中的人工智能
背景情况:
- 药物相互作用 (DDI) 带来了重大的临床风险,通常是由于抑制细胞P450 (CYP450) 酶造成的.
- 准确预测CYP450抑制剂对于安全的药物开发至关重要,但目前的机器学习方法缺乏精度和可解释性.
- 现有的方法很难充分利用多样化的分子数据来进行可靠的CYP450抑制预测.
研究的目的:
- 开发一种先进的机器学习模型,即多模式编码器网络 (MEN),用于对CYP450酶抑制剂的增强预测.
- 通过整合多种数据模式,提高CYP450抑制剂预测的准确性和生物解释性.
- 提供一种工具,帮助在药物开发管道的早期确定潜在的药物相互作用.
主要方法:
- 开发了一种多模式编码器网络 (MEN),集成了三种数据类型:化学指纹 (FEN),分子图 (GEN) 和蛋白质序列 (PEN).
- 采用每个数据模式的专用编码器来提取互补的特征,合并为全面的表示.
- 整合了一个可解释的AI (XAI) 模块,并使用可视化技术来对预测进行生物解释.
主要成果:
- 在五种CYP450异型 (1A2,2C9,2C19,2D6,3A4) 中,MEN的平均精度高达93.7%.
- 个别编码器表现出强的性能:FEN (80.8%),GEN (82.3%) 和PEN (81.5%).
- 取得了优异的整体性能指标,包括AUC (98.5%),灵敏度 (95.9%),特异性 (97.2%) 和MCC (88.2%).
结论:
- 通过有效地整合多模式分子数据,MEN模型显著提高了对CYP450抑制剂的预测.
- 集成的XAI模块增强了生物解释性,促进了对抑制机制的更深入理解.
- MEN为提高药物安全性和药物研发效率提供了一个有前途的工具.
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