对稳定挥发性药物的分子相互作用的基于结构的预测
Yuchen Zhao1, Danmei Bai1, Boyang Yang1
1Department of Pharmaceutics, School of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China.
Pharmaceutics
|January 28, 2026
概括
人工智能 (AI) 通过预测辅助剂相互作用来加速挥发性药物配方. 这项研究确定了大豆脂 (PC) 是d-borneol的最佳辅助剂,增强了纳米配方的稳定性.
科学领域:
- 制药科学 制药科学
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
背景情况:
- 挥发性药物由于高蒸发率而面临临临床限制.
- 传统的辅助剂查是低效的,资源密集的.
- 需要采用人工智能驱动的方法来优化药物辅助剂相互作用.
研究的目的:
- 开发一种人工智能策略,以有效选药物辅助剂相互作用.
- 确定适合稳定挥发性药物的辅助剂,如d-borneol.
- 为挥发性药品创建稳定的纳米配方.
主要方法:
- 利用Protenix结构预测进行d-borneol和472助剂的高通量模拟.
- 根据模拟选择了前50名候选人,随后进行了分子对接和稳定性实验.
- 对实验数据进行验证的AI预测.
主要成果:
- 人工智能预测显示了与实验验证的高度一致性.
- 大豆脂 (PC) 已成为d-borneol的最佳辅助剂.
- 基于PC的冷化脂质体配方显著降低了d-borneol的挥发,并提高了稳定性.
结论:
- 这项研究开创了用于挥发性药物辅助剂查的结构预测模型的使用.
- 通过使用AI识别的辅助剂成功开发了d-borneol的稳定纳米配方.
- 为挥发性制药纳米配方开发提供了一个新的范式.
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