评估基于纯溶剂溶解度的二元混合物参考溶剂溶解度计算的有效性:酸的情况
Piotr Cysewski1, Tomasz Jeliński1, Rafal Rozalski2
1Department of Physical Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950 Bydgoszcz, Poland.
Molecules (Basel, Switzerland)
|November 27, 2025
概括
本研究引入了一种机器学习 (ML) 框架,以准确预测活性药物成分 (API) 在二元溶剂混合物中的溶解度,仅使用纯溶剂数据和分子描述符. 开发的模型显著优于传统方法.
科学领域:
- 配方科学科学 配方科学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 在二元溶剂混合物中预测溶解度对于药物配方至关重要,但由于复杂的非添加剂混合效应而具有挑战性.
- 现有的基于物理学的模型往往难以准确地捕捉这些复杂的相互作用.
研究的目的:
- 开发一个强大的机器学习 (ML) 框架,用于准确预测二元溶剂混合物中的API溶解度.
- 仅使用纯溶剂数据和来自COSMO-RS.的分子描述符来进行预测.
主要方法:
- 在DMF,DMSO和4-FM的水性混合物中扩展了咖啡酸和酸的实验溶解度数据.
- 开发了DOO-IT (双目标优化与ITerative特征修剪) 方法论,用于自动描述符选择和超参数优化.
- 使用了通过稳定性分析识别的10描述器nuSVR模型进行预测.
主要成果:
- 在持久测试组中,在0.988的R2和0.0514的MAE上取得了出色的预测性能.
- 这种ML模型的性能明显优于标准的COSMO-RS方法.
- 选择的描述符表明模型学会纠正非理想的混合效应.
结论:
- 开发的ML框架提供了一个切实可行的工具,以减少对API溶解性的实验选.
- 这种可转移的方法使复杂化学系统的强有力的定量结构-属性关系 (QSPR) 建模成为可能.
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