在制药研究中用于分子设计的可解释的人工智能
Alec Lamens1,2, Jürgen Bajorath1,2
1Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, University of Bonn Friedrich-Hirzebruch-Allee 5/6 D-53115 Bonn Germany bajorath@bit.uni-bonn.de +49-228-7369-100.
Chemical science
|January 14, 2026
概括
可解释性AI (XAI) 对于理解分子设计中的机器学习 (ML) 预测至关重要. 整合领域知识增强了XAI,用于更好的模型改进和药物发现中的实验设计.
科学领域:
- 人工智能的人工智能
- 分子设计分子设计
- 计算化学的计算化学
背景情况:
- 机器学习 (ML) 模型,特别是深度学习,正在推进分子设计.
- 这些ML模型的"黑盒子"性质阻碍了对其预测的理解和接受.
- 可解释的人工智能 (XAI) 对于弥合这一差距至关重要,尤其是在实验科学中.
研究的目的:
- 检查XAI在分子设计中的挑战和机遇.
- 评估将特定领域的知识纳入XAI的好处.
- 讨论评估分子设计的化学语言模型的局限性.
主要方法:
- 在分子设计的背景下审查当前的XAI方法.
- 对XAI领域特定知识整合的分析.
- 讨论化学语言模型的评估.
主要成果:
- XAI方法需要提供以人为中心,透明和可解释的解释.
- 领域知识可以改进ML模型,帮助实验设计,并支持假设测试.
- 目前分子设计中化学语言模型的评估方法有限.
结论:
- XAI对于ML在分子设计中的实际应用至关重要.
- 将XAI与领域专业知识量身定制是释放其全部潜力的关键.
- 为了在药物发现中对人工智能工具进行强有力的评估,需要进一步开发.
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