使用生成性人工智能进行化学分类程序合成
Christopher J Mungall1, Adnan Malik2, Daniel R Korn3
1Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA. cjmungall@lbl.gov.
Journal of cheminformatics
|October 2, 2025
概括
生成型人工智能为生物兴趣化学实体 (ChEBI) 数据库创建化学分类程序. 这种方法提供了可解释的化学分类,补充了深度学习方法.
科学领域:
- 化学信息学和生物信息学
- 计算化学的计算化学
- 化学中的人工智能.
背景情况:
- 精确的化学结构分类对于各种应用至关重要,从药物发现到环境监测.
- 对于大型数据集,手动分类是低效的,而现有的自动化方法缺乏解释性或依赖于预定义的规则.
- 深度学习模型提供了高性能,但经常充当黑子,阻碍了可解释性.
研究的目的:
- 开发一种人工智能驱动的方法,用于自动生成化学分类程序.
- 创建一个可解释和可计算的化学命名体系的本体模型.
- 为了解决现有的自动化化学品分类方法的局限性.
主要方法:
- 利用生成型人工智能为ChEBI数据库类编写分类程序.
- 开发了ChEBI化学类程序本体学 (C3PO),用于可解释的SMILES结构的确定性分类.
- 与ChEBI数据库验证了该方法,并与深度学习和基于SMARTS的分类器进行了比较.
主要成果:
- 在C3PO的方法生成了能够确定性的运行时分类程序与自然语言解释.
- 与一个天真的 SMARTS 模式分类器相比,C3PO 显示出更高的性能.
- 虽然C3PO不匹配最先进的深度学习性能,但在可解释性和减少数据依赖性方面具有显著的优势.
结论:
- C3PO方法为化学分类提供了一个可解释和可计算的本体模型.
- 这种方法通过提供透明度来补充深度学习,并且可以同时用于验证的分类.
- C3PO程序可以帮助本体论的开发,并由人类策展人改进,以提高准确性和实用性.
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