mtADENet:一种新的可解释方法,集成了多种类型的基于网络的推理方法,用于预测不良药物事件
Zhuohang Yu1, Zengrui Wu1, Moran Zhou1
1Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China.
Computers in biology and medicine
|December 11, 2023
概括
mtADENet通过整合基于网络的推断,准确预测药物不良事件 (ADEs),帮助药物安全性评估和机制阐明. 这种新的方法通过早期识别潜在风险,提高了药物的发现和开发.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学的计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 药物不良事件 (ADEs) 在医疗保健和药物开发方面存在重大风险.
- 识别非目标相互作用是理解和预测ADEs的关键.
- 目前的方法往往难以弥合化学结构和观察到的ADEs之间的差距.
研究的目的:
- 开发一种新的,可解释的方法 (mtADENet) 来预测药物不良事件 (ADEs).
- 整合多种基于网络的推断方法,以提高ADE预测准确度.
- 为了解药物诱导ADEs背后的分子机制提供见解.
主要方法:
- 开发了mtADENet,一种基于网络的推断方法,集成计算目标配置文件.
- 使用基于网络的方法,为23个AEE类别构建了预测模型.
- 使用十倍交叉验证和四个外部数据集验证模型性能.
主要成果:
- 在ADE预测的交叉验证中达到高AUC值 (0.865-0.942).
- 与之前的基于网络的方法相比,在外部验证集上表现出优异的性能.
- 案例研究显示,通过文献对预测的ADEs和目标的验证率超过50%.
结论:
- mtADENet是一个强大而可解释的工具,用于预测ADEs和评估药物安全性.
- 该方法有效地弥合了化学结构和ADEs,有助于药物发现.
- mtADENet为分子机制提供了宝贵的见解,并通过NetInfer网络服务器提供.
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