Ensuring drug safety during pregnancy is critical due to the potential risks to both the mother and fetus. However, the exclusion of pregnant women from clinical trials complicates the assessment of adverse drug reactions (ADRs) in this population. This study aimed to develop and validate risk prediction models for pregnancy-related ADRs of drugs using advanced Machine Learning (ML) and Deep Learning (DL) techniques, leveraging real-world data from the FDA Adverse Event Reporting System. We explored three methods─Information Component, Reporting Odds Ratio, and 95% confidence interval of ROR─for classifying drugs into high-risk and low-risk categories. DL models, including Directed Message Passing Neural Networks (DMPNN), Graph Neural Networks, and Graph Convolutional Networks, were developed and compared to traditional ML models like Random Forest, Support Vector Machines, and XGBoost. Among these, the DMPNN model, which integrated molecular graph information and molecular descriptors, exhibited the highest predictive performance, particularly at the preferred term level. The model was validated against external data sets from SIDER and DailyMed, demonstrating strong generalizability. Additionally, the model was applied to assess the risk of 22 oral hypoglycemic drugs, and potential substructure alerts for pregnancy-related ADRs were identified. These findings suggest that the DMPNN model is a valuable tool for predicting ADRs in pregnant women, offering significant advancement in drug safety assessment and providing crucial insights for safer medication use during pregnancy.

中文翻译：

---

先进的 AI 驱动妊娠相关药物不良反应预测。


由于对母亲和胎儿都有潜在风险，确保怀孕期间的药物安全至关重要。然而，将孕妇排除在临床试验之外使该人群的药物不良反应 （ADRs） 评估复杂化。本研究旨在利用来自 FDA 不良事件报告系统的真实数据，使用先进的机器学习 （ML） 和深度学习 （DL） 技术开发和验证药物妊娠相关 ADR 的风险预测模型。我们探索了三种方法——信息成分、报告比值比和 ROR 的 95% 置信区间——将药物分为高风险和低风险类别。开发了 DL 模型，包括有向消息传递神经网络 （DMPNN）、图神经网络和图卷积网络，并与随机森林、支持向量机和 XGBoost 等传统 ML 模型进行了比较。其中，整合了分子图信息和分子描述符的 DMPNN 模型表现出最高的预测性能，尤其是在首选术语水平上。该模型针对来自 SIDER 和 DailyMed 的外部数据集进行了验证，显示出很强的泛化性。此外，该模型用于评估 22 种口服降糖药的风险，并确定了妊娠相关 ADRs 的潜在亚结构警报。这些发现表明，DMPNN 模型是预测孕妇 ADRs 的宝贵工具，为药物安全性评估提供了重大进展，并为怀孕期间更安全的药物使用提供了重要见解。