来自SMILES异型编码器的奇拉性描述符的评估
Natalia Baimacheva1, Xinyue Gao2, Joao Aires-de-Sousa3
1Faculty of Chemistry, University of Strasbourg, 4, Blaise Pascal Str., 67081, Strasbourg, France.
Journal of cheminformatics
|August 31, 2025
概括
使用隐藏空间向量 (LSV) 训练的机器学习模型显示出预测分子性. 虽然圆形指纹在预测性化顺序方面表现优于LSV,但差异LSV描述器提高了预测准确度.
科学领域:
- 计算化学
- 化学信息学
- 机器学习
背景情况:
- 在药物发现和开发中, 预测分子度至关重要.
- 传统的分子性表示方法有局限性.
- 潜在空间向量 (LSV) 为分子表示提供了一种新的方法.
研究的目的:
- 探索从SMILES异型编码器中衍生出的LSV来预测奇拉性质.
- 将LSV的性能与传统的圆形指纹进行比较.
- 研究潜在空间算法以增强奇拉表示.
主要方法:
- 使用SMILES异型编码器的LSV训练的机器学习模型.
- 采用潜空间算法,包括体和立体化学耗尽的差异.
- 在3858个分子的数据集上使用随机森林算法.
- 在Chiralpak® AD-H柱和内在性标签上预测的化顺序.
主要成果:
- 在预测化顺序方面,LSV衍生的描述器达到0.75的准确性.
- 传统的圆形指纹可以达到0.82的精度.
- 不同的LSV描述器表现出比原来的LSV描述器更好的预测能力.
结论:
- LSV 显示可能代表分子性.
- 目前,圆形指纹在预测性化顺序方面具有卓越的性能.
- 潜在空间算法增强了基于LSV的奇拉性描述器的预测能力.
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