多种FG:通过注意力机制集成分子指纹和图形嵌入,以进行可靠的药物副作用预测
Zuhai Hu1, Jinxiang Yang1, Linghao Ni1
1College of Public Health, Chongqing Medical University, Chongqing, 401331, China.
Scientific reports
|July 4, 2025
概括
一个新的深度学习模型,多指纹和图形嵌入 (MultiFG),准确地预测药物副作用频率和关联. 这一进步有助于对已批准和新药的药物安全性评估.
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
- 人工智能的人工智能
背景情况:
- 准确预测药物副作用频率对于药物安全至关重要,但在成本和模型通用性方面面临挑战.
- 现有的模型经常与药物诱导的不良事件的复杂性和多样性作斗争.
研究的目的:
- 开发一个新的深度学习框架,多指纹和图形嵌入 (MultiFG),用于预测药物副作用的关联和频率.
- 提高计算药物安全性评估模型的准确性和通用性.
主要方法:
- MultiFG集成了多种分子指纹,基于图形的嵌入和药物副作用相似性特征.
- 该框架使用注意力增强的卷积网络和Kolmogorov-Arnold网络 (KAN) 来进行复杂的关系建模.
- 对预测已批准和新药的副作用关联和频率进行了性能评估.
主要成果:
- 在预测副作用关联方面,MultiFG获得了高性能 (AUC 0.929,精度@15 0.206,回忆@15 0.642),超过了最先进的方法.
- 对于副作用频率预测,MultiFG表现出显著的改善,RMSE为0.631和MAE为0.471.
- 该模型显示了强大的概括能力,用于预测新药副作用.
结论:
- 多FG在预测药物副作用的相关性和频率方面取得了重大进展.
- 该模型提供了一种实用而强大的工具,用于增强市场和研究药物的药物风险评估.
- 这种深度学习方法解决了当前药物安全评估方法的局限性.
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