t-SMILES:一个基于片段的分子表示框架,用于新的配体设计
Juan-Ni Wu1, Tong Wang1, Yue Chen1
1State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha, 410082, PR China.
Nature communications
|June 11, 2024
概括
这项研究介绍了基于树的SMILES (t-SMILES),一种新的分子表示框架. t-SMILES通过提供灵活的多代码描述来提高人工智能模型在药物发现中的性能.
科学领域:
- 计算化学和化学信息学.
- 开发用于人工智能的新型分子表示方法.
背景情况:
- 有效的分子表示对于化学中的AI模型性能至关重要.
- 像SMILES这样的现有方法在捕捉复杂的分子特征方面存在局限性.
研究的目的:
- 引入一种灵活的,基于片段的,多尺度的分子表示框架,称为t-SMILES (基于树的SMILES).
- 评估t-SMILES及其三个编码算法 (TSSA,TSDY,TSID) 的性能,并将其与现有方法进行比较.
主要方法:
- 开发了t-SMILES,这是一个框架,通过从碎片化分子图中对二进制树的宽度首次搜索生成SMILES类型的字符串.
- 采用了三个编码算法:TSSA (共享原子),TSDY (没有ID的虚拟原子) 和TSID (ID和虚拟原子).
- 使用JTVAE,金,MMPA,Scaffold和基准数据集 (ChEMBL,,QM9) 进行了系统评估.
主要成果:
- 证明了多代码分子描述系统的可行性,其中代码相互补充,以提高性能.
- 在低资源数据集上表现出更好的性能,避免过度匹配和增加新性,同时保持相似性.
- 在目标定向任务和基准数据集中明显优于经典的SMILES,DeepSMILES,SELFIES和基线模型.
结论:
- t-SMILES为人工智能提供了一个强大而灵活的分子表示框架.
- 多代码系统增强了模型的性能,新性和概括性.
- 与现有的分子表示技术相比,t-SMILES是一个显著的进步.
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