基于机器学习的药物相互作用预测的全面审查:当前状态,挑战和机会
Ning-Ning Wang1,2, Bei Zhu3, Xin-Liang Li1,2
1Department of Pharmacy, Xiangya Hospital, Central South University, Changsha 410008, Hunan, P.R. China.
Journal of chemical information and modeling
|December 22, 2023
概括
机器学习 (ML) 为检测药物相互作用 (DDI) 提供了可靠的替代方案,克服了实验的局限性. 这篇评论探讨了基于ML的DDI预测,涵盖数据库,药物属性和流行的算法.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 人工智能的人工智能
背景情况:
- 药物相互作用 (DDI) 在药物开发和管理中至关重要.
- 实验性DDI检测方法有局限性,导致需要替代方法.
- 机器学习 (ML) 已经成为DDI预测的强大工具.
研究的目的:
- 系统地审查基于机器学习的药物相互作用预测研究.
- 提供有关DDI研究现状和未来方向的见解.
- 为了比较不同的ML方法,药物属性和用于DDI检测的数据库.
主要方法:
- 关于ML用于DDI预测的最新研究的文献综述.
- 基于DDI数据库,药物属性和ML方法的研究分类.
- 对浅层学习,深度学习,推系统和基于知识图的方法进行比较分析.
主要成果:
- 主要DDI数据库的概述 (药物,副作用,相互作用信息).
- 通常使用的药物属性的摘要 (化学,生物,表型).
- 对用于DDI检测的流行的ML方法的分析.
结论:
- 基于ML的DDI预测是一个快速发展的领域.
- 未来的研究应该涉及数据集成,模型解释性和现实世界的验证.
- 在开发更强大,更可通用的DDI预测系统方面存在机遇.
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