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MASMDDI:用于药物相互作用预测的多层自适应软面具图形神经网络
Junpeng Lin1, Binsheng Hong1, Zhongqi Cai1
1School of Computer and Information Engineering, Xiamen University of Technology, Xiamen, China.
Frontiers in pharmacology
|June 4, 2024
概括
预测药物相互作用 (DDI) 对患者安全至关重要. 一种名为MASMDDI的新方法使用图形神经网络来分析药物亚结构,比现有的方法提高了DDI预测的准确性.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 人工智能在药物发现中的作用
背景情况:
- 准确的药物相互作用 (DDI) 预测对于预防组合治疗中药物不良反应至关重要.
- 现有的DDI预测方法往往忽略了关键的化学基层相互作用,限制了它们的预测能力.
- 需要先进的计算模型来捕获详细的分子亚结构信息,以改进DDI预测.
研究的目的:
- 推出一种新的多层自适应软面具图形神经网络 (MASMDDI),用于增强DDI预测.
- 通过结合化学基结构相互作用来解决当前方法的局限性.
- 提高预测潜在药物不良事件的准确性和可靠性.
主要方法:
- 开发了一种多层适应性软面具图形神经网络,从分子图形中提取相关的子结构.
- 使用注意力机制来挖掘亚结构特征并更新隐藏的表示.
- 将药物相互作用分解为核心药物基结构之间的对对相关性,以优化特征表示.
主要成果:
- 拟议的MASMDDI模型在DDI预测任务中明显优于最先进的方法.
- 在预测以前未知的药物的DDI方面,MASMDDI表现出色.
- 在使用DrugBank数据集的转导场景中获得了高准确度得分 (ACC: 0.9596,AUROC: 0.9903,AUPRC: 0.9894),超过了基线性能2%.
结论:
- 通过有效利用化学亚结构信息,MASMDDI为DDI预测提供了一种优越的方法.
- 该模型能够预测未知药物的相互作用,这突显了其在现实世界临床应用中的潜力.
- 这种新的方法代表了计算药物安全和发现的重大进步.
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