可解释的人工智能:药物发现的视角
Yazdan Ahmad Qadri1, Sibhghatulla Shaikh2,3, Khurshid Ahmad4
1School of Computer Science and Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of Korea.
Pharmaceutics
|September 27, 2025
概括
可解释的人工智能 (XAI) 提高了人工智能驱动的药物发现的透明度,解决了"黑子"问题. XAI方法加快治疗目标的识别和简化药物开发管道.
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.
- 人工智能的人工智能是人工智能.
背景情况:
- 人工智能 (AI) 和深度学习 (DL) 加快了药物发现,但存在"黑子"问题,阻碍了制药研究人员的采用.
- 可解释的人工智能 (XAI) 通过增加对AI模型的透明度和信任来提供解决方案.
研究的目的:
- 系统地审查XAI的原则,方法和药物发现工具.
- 探索XAI在加速药物发现管道的各个阶段的应用.
- 检查XAI如何解决AI模型不透明性及其挑战.
主要方法:
- 系统地调查XAI的原则和方法.
- 审查XAI工具,模型和药物发现框架.
- 深入讨论XAI在医疗保健和药物开发中的应用.
主要成果:
- 在AI驱动的药物发现中,XAI提高了透明度,信任和可靠性.
- XAI的应用包括分子建模,目标识别,ADME预测和临床试验设计.
- XAI有效地弥合了计算预测和制药应用之间的差距.
结论:
- 在药物发现中,XAI对于克服AI的"黑盒"性质至关重要.
- 需要进一步的研究来应对部署XAI方法的挑战.
- 保持最新的XAI技术对于提高药物发现效率和临床影响至关重要.
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