从黑盒子到可操作的洞察力:对科学发现的可解释人工智能的观点
Zhenxing Wu1,2, Jihong Chen1,2, Yitong Li1
1Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058 Zhejiang, P. R. China.
Journal of chemical information and modeling
|December 11, 2023
概括
可解释的人工智能 (XAI) 为化学家们提供了对复杂机器学习模型的洞察力. 本综述涵盖了XAI方法,案例研究和化学人工智能的未来挑战.
科学领域:
- 化学 化学 化学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 化学中的黑盒机器学习模型缺乏透明度.
- 可解释的人工智能 (XAI) 正在出现,以解决这个差距.
- XAI为化学预测提供了理由和可操作的见解.
研究的目的:
- 调查和分类适用于化学的XAI技术.
- 通过案例研究来说明XAI的实际实用性.
- 讨论XAI在化学中的挑战和未来方向.
主要方法:
- 对适用于化学应用的XAI技术进行文献综述.
- 基于技术细节的XAI方法的分类.
- 展示了XAI实际应用的案例研究.
主要成果:
- 一系列适用于化学应用的XAI技术已被确定和分类.
- 案例研究展示了XAI在识别致癌分子和指导分子优化方面的实用性.
- 挑战包括开发可靠的解释,确保稳定性和定制.
结论:
- 在化学领域推进机器学习方面,XAI至关重要.
- 应对当前的挑战和采用大型语言模型等新技术将巩固人工智能的作用.
- XAI使化学家能够从复杂的模型中获得可解释和可操作的见解.
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