基于SELFIES的分子描述器,结构生成和反向QSAR/QSPR
1Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.
ACS omega
|June 26, 2023
概括
本研究引入了用于强大的分子设计的自我引用嵌入字符串 (SELFIES),使得分子描述符和结构之间直接一对一的映射成为成功的反向定量结构-活动关系 (QSAR) 和定量结构-属性关系 (QSPR) 建模.
科学领域:
- 计算化学计算化学
- 化学信息学 化学信息学
- 药物发现 药物发现 药物发现
背景情况:
- 传统的反向量化结构-活动关系 (QSAR) 和量化结构-属性关系 (QSPR) 方法需要生成许多化学结构并计算它们的描述符,通常缺乏直接的结构-描述符对应.
- 这种限制阻碍了高效的分子设计和属性预测.
研究的目的:
- 提出一种用于反向QSAR/QSPR的新方法,使用自我引用嵌入字符串 (SELFIES),一个强大的分子表示.
- 建立分子描述符和化学结构之间的一对一映射,以改进反向QSAR/QSPR.
- 为了证明具有目标性质的分子的成功生成.
主要方法:
- 自拍字符串被转换成一热向量来导出自拍描述符 (x).
- 使用这些描述符和客观变量 (y) 进行了QSAR/QSPR模型 (y = f(x)) 的反向分析.
- 基于SELFIES的结构生成被用来创建与特定描述符值相对应的分子.
主要成果:
- 拟议的SELFIES描述符和结构生成方法在真实复合数据集上得到了验证.
- 基于SELFIES描述符的QSAR/QSPR模型的预测性能与基于指纹的现有模型相美.
- 成功生成了大量与SELFIES描述值一对一关系的分子.
结论:
- SELFIES的代表性为反向QSAR/QSPR提供了一个强大而高效的框架.
- 这种方法可以直接生成具有所需性质的分子,克服传统方法的局限性.
- 该研究成功地证明了SELFIES在产生具有特定向性质的分子中的应用.
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