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一种基于相似性的简化方法,用于药物相互作用的预测
Guy Shtar1,2, Adir Solomon2, Eyal Mazuz1
1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
PloS one
|November 9, 2023
概括
早期预测药物相互作用 (DDI) 对安全至关重要. 这项研究引入了一种基于相似性的新方法,LAMFP,用于仅使用化学结构的临床前DDI预测,在未见药物上实现高精度.
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.
- 药物发现 药物发现
背景情况:
- 药物相互作用对于药物安全监测至关重要.
- 实验室DDI检测是昂贵和耗时的,需要in-silico方法.
- 现有的机器学习模型通常需要晚期数据,限制临床前应用.
研究的目的:
- 开发一种有效的in-silico方法,用于仅使用化学结构进行临床前DDI预测.
- 为应对预测新的,未见过的药物的DDI的挑战.
- 通过专注于早期药物开发,改进现有的DDI预测模型.
主要方法:
- 提出了一种基于相似性的新方法,搜索邻近矩阵因子化与传播 (LAMFP),扩展AMFP用于未见的分子.
- 利用基于化学结构的查找机制进行DDI预测.
- 在回顾性分析中,对LAMFP药物进行了测试,这些药物被添加到后期版本的DrugBank数据库中.
- 评估了一组相似度指标和Chemprop,一个传递信息的神经网络.
主要成果:
- 在预测DDI方面,LAMFP取得了很高的准确性,接收器操作特征曲线下的面积为0.82.2.
- 该方法在涉及新药和现有药物的相互作用以及现有药物之间的相互作用方面表现强.
- 对于DDI预测,LAMFP的表现优于最先进的图形神经网络方法.
结论:
- LAMFP提供了一种简单而有效的解决方案,用于仅使用化学结构进行临床前DDI预测.
- 开发的方法准确地预测了未见药物的DDI,支持早期药物安全性评估.
- 这种方法通过为早期DDI检测提供可靠的工具,促进了在体中药物安全.
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