ML驱动的制药共晶技术:查,属性预测和应用方面的进展
Qian Ye1, Sheng Wang2, Zhaoyang Zhang3
1School of Pharmacy, Anhui Medical University, Hefei, 230032, China.
AAPS PharmSciTech
|January 8, 2026
概括
制药共晶技术提高了药物的生物可用性和特性. 机器学习 (ML) 模型加快了新型药物共晶体的选和开发,提高了疗效和患者遵守.
科学领域:
- 制药科学 制药科学
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 制药共晶技术因其创新性和可持续性而受到全球关注.
- 晶增强生物可用性,并优化可溶性差的药物的物理化学和生物特性.
研究的目的:
- 开发一个全面的框架,以了解药物结晶的定义,查,特征和应用.
- 突出创新的选方法,特别是机器学习 (ML) 算法,用于药物共晶体的开发.
主要方法:
- 讨论了创新的选方法,包括高通量计算选,人工智能 (AI) 和拉曼光谱.
- 专注于机器学习 (ML) 算法的应用,用于虚拟共晶查和可溶性预测.
- 将ML模型与X射线衍射,热分析 (TA) 和高性能液态色谱 (HPLC) 等传统实验方法进行了比较.
主要成果:
- 机器学习模型可以分析大型物理化学数据集,用于虚拟查和可溶性预测.
- 与传统方法相比,ML模型可以管理高的工作负载,解决选缺口,并促进准确的溶解率预测.
- 通过缩小实验范围,ML加速了新型药物共晶的发现和开发.
结论:
- 通过ML增强的药物共晶技术,有望提高药物的疗效,减少不良反应,并改善患者的遵守性.
- 整合实验和虚拟选方法对于提高共晶选择效率和准确性至关重要.
- 该框架通过共结晶来优化药物特性来支持当代药物开发.
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