使用图表特征和注意力机制预测可疑引起药物不良反应的药物
Jinxiang Yang1, Zuhai Hu1, Liyuan Zhang1
1College of Public Health, Chongqing Medical University, Chongqing 401331, China.
一个新的深度学习模型识别了导致药物不良反应 (ADRs) 的药物. 这种方法有助于预测药物的副作用,并提高预防有害事件的意识.
科学领域:
- 药物监督 药物监督 药物监督
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
背景情况:
- 药物不良反应 (ADR) 是药物的意外有害影响,导致大量住院和医疗费用.
- 提高对ADRs的认识对于预防至关重要,不良事件中药物评估是关键策略.
- 副作用是一个主要的公共卫生问题,需要先进的识别和预测方法.
研究的目的:
- 开发和验证一种新型模型,用于识别与不良事件相关的疑似药物.
- 预测各种药物的潜在不良药反应 (ADR).
- 探索该模型在更广泛的药物发现和分类任务中的实用性.
主要方法:
- 一个可疑药物辅助判断模型 (SDAJM) 使用图形异构网络 (GIN) 和注意力机制设计.
- 该模型从患者人口统计,药物信息和药物不良反应数据中提取特征.
- 进行特征提取以确定可表明药物诱导不良事件的模式.
主要成果:
- 与其他模型相比,SDAJM在预测可疑药物的不良反应事件方面表现强.
- 对心血管和抗甲状腺药物的病例分析证实了该模型在预测药物诱导的副作用方面的能力.
- 在基准数据集 (Tox21,SIDER) 上的验证证实了该模型对药物发现中的分类任务的适用性.
结论:
- 使用深度学习的SDAJM有效地识别了导致药物不良事件 (ADEs) 的药物.
- 该模型有助于预测药物不良反应,并支持其他药物发现工作.
- 这项研究为推进ADR研究和药物监管领域提供了新的方法.
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