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人工智能用于药物诱导的肝损伤预测:一个系统性审查

Yves M K Kantagba^1,2, Seydou G Barro^1,2,3, Serge L W Nikiema^1,2

¹Université NAZI BONI, Bobo Dioulasso, Burkina-Faso.

Studies in health technology and informatics

|July 1, 2025

概括

人工智能 (AI) 模型可以预测药物诱导性肝损伤 (DILI),减少昂贵的临床试验失败. 机器学习算法,特别是随机森林,在药物开发过程中评估肝损伤风险方面表现有前途.

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
毒理学毒理学毒理学

背景情况:

药物毒性和副作用经常导致临床试验失败,导致大量的财务和时间损失.
药物诱导性肝损伤 (DILI) 是临床开发阶段药物排斥的主要原因.
机器学习 (ML) 模型提供了一个有希望的方法来预测DILI,并取得了显著的成功.

研究的目的:

系统地审查和描述用于预测药物诱导性肝损伤 (DILI) 的人工智能 (AI) 策略.
识别和分析DILI预测模型中使用的AI算法和特征.

主要方法:

系统的文献审查使用关键词在谷歌学者和PubMed数据库.
文章的选择和分析遵循了PRISMA指南.
识别的AI算法包括随机森林,支持矢量机,决策树,神经网络,K-最近邻居,XGBoosting和物流回归.

主要成果:

随机森林算法在单个研究中的其他模型相比显示出更高的性能.
人工智能模型整合了各种特征,如物理化学特性,分子指纹和基因表达数据.
美国FDA DILI 排名数据库作为主要药物来源;使用组合方法 (如投票组合) 来提高准确性.

结论:

人工智能是药物开发中风险评估的宝贵工具,有助于早期识别潜在的肝损伤.
多样化的数据特征和先进组合策略的整合提高了AI模型对DILI的预测能力.
人工智能对降低风险和优化药物研发管道做出了重大贡献.

关键词:

药物诱导的肝损伤是药物诱导的肝损伤.机器学习是机器学习.预测预测预测

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