在QSPR中简单性和准确性的二元性:用于预测选定药用酸在深层欧性溶剂中的溶解度的机器学习框架
Piotr Cysewski1, Tomasz Jeliński1, Julia Giniewicz1
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
这项研究使用机器学习来预测药物酸在深度欧性溶剂 (DES) 中的溶解度. DOO-IT框架优化模型的准确性和简单性,有助于药物开发的溶剂选择.
科学领域:
- 物理化学 物理化学
- 计算化学是一种计算化学.
- 药物开发 药物开发
背景情况:
- 深度浸泡溶剂 (DES) 为制药应用提供可调节的特性.
- 预测DES等新型溶剂中的药物溶解度对于配方开发至关重要.
- 现有的模型可能无法完全捕捉控制DESs溶解度的复杂相互作用.
研究的目的:
- 使用机器学习系统地研究药品酸在各种DES中的溶解度.
- 开发准确和节的定量结构-属性关系 (QSPR) 模型用于溶解性预测.
- 探索DES系统中模型复杂性,成本和预测能力之间的权衡.
主要方法:
- 使用了一种自动化的双目标优化与代特征修剪 (DOO-IT) 框架.
- 在DES中编制和增强了10个碳酸的溶解度数据集,共计1020个数据点.
- 采用数据驱动的多标准测量方法来选择准确,简单和持久的QSPR模型.
主要成果:
- 确定了关键的能量描述符,可以准确地预测DESs中的制药酸溶解度.
- 证明这些描述符可以补充或取代诸如COSMO-RS.这样的计算密集型方法.
- 该DOO-IT框架促进了模型超空间的高效探索,并确定了最佳模型.
结论:
- 机器学习,特别是DOO-IT框架,提供了一种有效的方法来模拟DESs中的溶解性.
- 在物理上有意义的描述符为DES系统中的QSPR提供了有价值的替代方案.
- 这项研究有助于合理选择用于使用DESs的制药配方的溶剂.
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