提高分子字符串表示用于生成化学的可靠性
Etienne Reboul1,2, Zoe Wefers2, Harish Prabakaran1
1Université Paris Cité, CNRS, Laboratoire de Biochimie Théorique, Paris 75005, France.
Journal of chemical information and modeling
|September 17, 2025
概括
选择正确的分子表示是生成化学模型的关键. 与SMILES和SELFIES相比,一种新的方法ClearSMILES显著提高了生成分子的有效性和准确性.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
背景情况:
- 化学中的生成建模正在迅速发展.
- 微笑是一种常见但有缺陷的分子表示,用于生成任务.
- 自拍确保了分子的有效性,但可能缺乏忠实性.
研究的目的:
- 为生成模型全面评估SMILES和SELFIES.
- 评估分子生成的可行性和可靠性.
- 开发改进的数据增强策略.
主要方法:
- 使用可行性和忠诚度指标对SMILES和SELFIES的评估.
- 开发数据增强程序,用于两个表示.
- 介绍ClearSMILES,这是SMILES的一个随机增强方法.
主要成果:
- 在RDKit规范的微笑中,20%的时间产生了无效分子.
- 自拍生成了有效的分子,但对训练数据的忠诚度很低.
- ClearSMILES将无效样本减少到2.2%并提高了保真度.
结论:
- 无论是微笑还是自拍,都不是单独用于生成分子建模的最佳选择.
- ClearSMILES增强了SMILES,提供了一个更强大的代表性.
- 数据增强对于提高分子生成质量至关重要.
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