先进的AI驱动预测与怀孕相关的不良药物反应
Jinfu Peng1, Li Fu1, Guoping Yang1,2
1Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Changsha 410031, Hunan, China.
Journal of chemical information and modeling
|November 29, 2024
概括
在怀孕期间预测药物风险至关重要. 一个新的定向消息传递神经网络 (DMPNN) 模型使用现实世界的数据准确地识别了孕妇潜在的不良药物反应 (ADR).
科学领域:
- 药监和药物安全 药监和药物安全
- 计算化学和化学信息学
- 在医疗保健中的机器学习和人工智能
背景情况:
- 在孕妇中评估药物安全性是具有挑战性的,因为它们被排除在临床试验之外.
- 现实世界的数据,如FDA不良事件报告系统 (FAERS),为药物风险提供了有价值的见解.
- 需要先进的计算方法来预测与怀孕相关的药物不良反应 (ADR).
研究的目的:
- 开发和验证机器学习 (ML) 和深度学习 (DL) 模型,用于预测与怀孕相关的副作用.
- 通过先进的预测建模,识别孕妇使用高风险药物.
- 加强怀孕期间使用药物的药物安全性评估.
主要方法:
- 利用了来自FDA不良事件报告系统 (FAERS) 的现实数据.
- 开发并比较各种ML模型 (随机森林,SVM,XGBoost) 和DL模型 (DMPNN,GNN,GCN).
- 采用信息组件和报告几率比率 (ROR) 等方法进行风险分类,DMPNN集成分子图形信息和描述符.
主要成果:
- 定向消息传递神经网络 (DMPNN) 模型表现出卓越的预测性能,特别是在首选术语层面.
- 在与外部数据集 (SIDER,DailyMed) 相比验证时,DMPNN模型显示出强大的通用性.
- 对口服低血糖药物的应用确定了与怀孕相关的副作用的潜在基结构警报.
结论:
- DMPNN模型是预测怀孕人口中与药物相关的副作用的强大和经过验证的工具.
- 这种方法显著提高了怀孕期间药物的药物安全性评估.
- 这些发现为孕妇更安全地使用药物提供了关键的见解.
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