通过深度学习提高对小数据集的预测模型的准确性,以抑制细胞染色体P450的作用
Elpri Eka Permadi1,2, Reiko Watanabe3, Kenji Mizuguchi4
1Laboratory for Computational Biology, Institute for Protein Research, Osaka University, 3-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
Journal of cheminformatics
|April 30, 2025
概括
深度学习通过识别P450 (CYP) 抑制剂来准确预测药物相互作用,即使数据有限. 使用图形卷积网络和数据归算的多任务模型显著改善了对药物安全性的预测.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
- 药物新陈代谢 药物新陈代谢
背景情况:
- 细胞染色体P450 (CYP) 酶对于药物代谢至关重要,但药物诱导的抑制会导致不良相互作用.
- 预测CYP抑制对于安全的药物开发和管理至关重要.
- 对于特定的CYP异型如CYP2B6和CYP2C8的有限数据是一个重大挑战.
研究的目的:
- 研究深度学习技术对预测CYP抑制的有效性,特别是对于数据稀缺的异构体.
- 为了利用相关CYP异形的数据,并使用数据归算来提高预测模型的性能.
- 在已批准的药物中识别潜在的CYP2B6和CYP2C8抑制剂.
主要方法:
- 从公共数据库中编制了12369种IC50值的化合物的数据集,涵盖7种CYP异型.
- 开发和比较单任务,微调和多任务深度学习模型,包括那些对缺失值进行数据归算的模型.
- 在多任务框架内使用图形卷积网络架构.
主要成果:
- 结合数据归算的多任务模型在预测CYP抑制方面显著优于单任务模型.
- 最准确的模型在1808种已批准的药物中确定了161种潜在的CYP2B6抑制剂和154种潜在的CYP2C8抑制剂.
- 数据归算技术显著提高了对CYP2B6和CYP2C8抑制的预测准确性.
结论:
- 多任务深度学习,特别是使用图形卷积网络和数据归算,有效地提高了CYP抑制预测的准确性.
- 这种方法表明,即使使用有限的特定异形数据,通过利用相关信息,也可以构建准确的预测模型.
- 这些发现为评估药物相互作用风险和改善药物安全性概况提供了有价值的工具.
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