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基于机器学习的药物相互作用预测:对模型,局限性和数据挑战的批判性审查
Flaviu-Ioan Gheorghita1, Vlad-Ioan Bocanet2, Laszlo Barna Iantovics3
1Doctoral School of Letters, Humanities and Applied Sciences, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureş, Târgu Mureş, Romania.
Frontiers in pharmacology
|August 14, 2025
概括
计算方法,包括机器学习,增强药物相互作用预测 (DDIp) 以减少实验成本. 本次审查强调了DDIP技术的进步及其局限性.
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.
- 生物信息学是一种生物信息学.
背景情况:
- 药物相互作用 (DDI) 是复杂的,并未完全理解.
- 通过计算预测DDI可以减少对昂贵的体外实验的需求.
- 机器学习和深度学习为DDI预测提供了有希望的方法.
研究的目的:
- 审查最近在计算药物相互作用预测 (DDIp) 的进展.
- 分析DDIp应用的最先进的机器学习技术.
- 确定该领域的共同挑战和未来研究方向.
主要方法:
- 系统审查来自Web of Science的24篇相关论文.
- 专注于用于DDI预测的机器学习算法.
- 对方法,结果,局限性和未来方向的分析.
主要成果:
- 机器学习,统计和深度学习方法是DDIP的关键.
- 技术包括半监督,监督,自我监督,基于图形和矩阵的分解.
- 关键的局限性包括阶级不平衡和新药的表现不佳.
结论:
- 计算DDIp对于制药研究至关重要.
- 需要进一步的研究来解决诸如可解释性和数据稀缺等局限性.
- 机器学习的进步对于准确的DDI预测至关重要.
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