人工智能驱动的药物毒性预测:进步,挑战和未来方向
Ruiqiu Zhang1,2, Hairuo Wen2,3, Zhi Lin2,3
1National Institutes for Food and Drug Control, Chinese Academy of Medical-Sciences and Peking Union Medical College, Beijing 100730, China.
Toxics
|July 25, 2025
概括
人工智能 (AI) 正在彻底改变药物毒性预测,克服传统方法的局限性. 这篇评论详细介绍了人工智能.
科学领域:
- 计算毒理学计算毒理学
- 药物的发现和开发.
- 人工智能在医学中的应用
背景情况:
- 传统的药物毒性预测方法面临诸如高成本,低吞吐量和跨物种推断不确定性等挑战.
- 这些局限性阻碍了高效的新药研发.
- 人工智能 (AI) 为药物毒理学评估提供了一种变革性的方法.
研究的目的:
- 系统地审查全球文献和AI应用在药物毒性预测中的发展状态.
- 分析不同毒性终点的各种毒性数据库和预测方法的利用情况.
- 讨论人工智能在药物毒性预测中的进展,优势,挑战和未来方向.
主要方法:
- 综合文献综述,重点关注AI (机器学习,深度学习) 在药物毒性预测中的应用.
- 分析毒性数据库及其在预测建模中的作用.
- 对各种毒性终点 (例如,急性毒性,致癌性,器官特异性毒性) 的预测结果和方法的深入研究.
主要成果:
- 人工智能,特别是深度学习和多式联通数据融合,正在显著推进药物毒理学评估.
- 各种人工智能模型已经证明在预测各种毒性终点方面具有有效性.
- 该审查综合了当前的研究,突出了成功的应用,并确定了关键趋势.
结论:
- 人工智能带来了强大的范式转变,提高了药物毒性预测的效率和可靠性.
- 人工智能技术为改善开发管道中的药物安全评估提供了可行的策略.
- 人工智能的持续研究和开发对于预测毒理学的未来进步至关重要.
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