基于扩散的生成药物样分子编辑与化学自然语言
Jianmin Wang1, Peng Zhou2, Zixu Wang3
1Department of Integrative Biotechnology, Yonsei University, Incheon, 21983, South Korea.
Journal of pharmaceutical analysis
|July 18, 2025
概括
这项研究介绍了DiffIUPAC,一种用于将化学自然语言 (国际纯化学和应用化学名称联盟) 转换为分子结构 (简化分子输入线输入系统字符串) 的新型扩散模型,从而推进了分子设计.
科学领域:
- 计算化学是一种计算化学.
- 化学领域的人工智能
- 药物发现信息学 药物发现信息学
背景情况:
- 扩散模型对于分子设计是有效的,但主要关注图形或3D结构.
- 分子序列扩散模型的探索较少,尽管化学命名的语言性质.
- 国际纯化学和应用化学联盟 (IUPAC) 的名称代表化学结构作为自然语言.
研究的目的:
- 开发一种能够将IUPAC名称转换为简化分子输入线输入系统 (SMILES) 字符串的扩散模型.
- 调查预训练的扩散模型性能是否可转移到化学自然语言处理.
- 为了使可控制的分子编辑从化学自然语言到化学语言.
主要方法:
- 提出了DiffIUPAC,一种用于分子编辑的有条件扩散模型.
- 使用IUPAC名称作为输入来生成相应的SMILES字符串.
- 评估模型性能与现有方法相比,并分析化学空间和脚手架多样性.
主要成果:
- 与现有方法相比,DiffIUPAC表现出优越的性能.
- 该模型成功地学习并应用了IUPAC和SMILES的语义规则.
- 生成的分子表现出支架的多样性,同时坚持输入约束.
结论:
- DiffIUPAC使用扩散模型有效地将化学自然语言 (IUPAC) 转换为化学语言 (SMILES).
- 该模型显示了药物设计中的应用潜力,包括功能组编辑,模拟生成和链接器设计.
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