达普在深层溶解剂中的溶解性:实验分析,分子洞察力和机器学习预测
Tomasz Jeliński1, Maciej Przybyłek1, Rafał Różalski2
1Department of Physical Chemistry, Faculty of Pharmacy, Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Poland.
Polimery w medycynie
|January 10, 2024
概括
深度浸泡溶剂 (DES) 显著提高了达 (DAP) 的溶解度,为传统溶剂提供了更绿色的替代品. 通过新数据改进的机器学习模型,可以准确地预测各种溶剂系统的溶解度.
科学领域:
- 制药科学 制药科学
- 物理化学 物理化学
- 计算化学计算化学
背景情况:
- 达 (DAP) 是一种重要的抗炎和抗菌剂,特别是在与艾滋病相关的疾病中.
- 低溶解度限制了DAP的治疗效果和配方选择.
研究的目的:
- 为了探索深层水溶剂 (DES) 作为达 (DAP) 的新型溶解剂.
- 为了阐明和量化DAP-DES系统内的分子间相互作用.
- 通过使用DES的实验数据来增强机器学习 (ML) 溶解度预测模型.
主要方法:
- 通过将胆化物 (ChCl) 与各种多聚醇结合,合成了新的DES.
- 在DES中通过光谱测量量量化DAP溶解度,并评估水的共同溶剂效应.
- 对ML模型描述器的研究分子间相互作用和热力学特性.
主要成果:
- 与传统有机溶剂相比,新配方的DES显示出更高的DAP溶解度.
- 水作为有效的辅溶剂,增加了DAP在DES中的可溶性.
- 用DES数据更新的ML模型显示,DAP溶解度的预测准确性得到改善.
结论:
- 开发的DES是有效的,环保的系统,可以提高DAP的溶解度.
- 了解分子相互作用是优化溶解度和预测建模的关键.
- 机器学习模型需要通过新的实验数据进行持续的改进,以便进行可靠的预测.
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