机器学习分析药物共晶的可溶性参数,以增强先进制药制造的药物特性
Tareq Nafea Alharby1, Bader Huwaimel2,3
1Department of Clinical Pharmacy, College of Pharmacy, University of Ha'il, Ha'il, 81442, Saudi Arabia. tn.alharby@uoh.edu.sa.
Scientific reports
|August 15, 2025
概括
一个新的机器学习框架预测了医药用共晶体的汉森溶解度参数. 核心回归 (KRR) 显示出卓越的性能,有助于药物设计和可溶性增强.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 制药科学 制药科学
背景情况:
- 预测汉森溶解度参数 (HSP) 对于开发具有增强性质的制药共晶非常重要.
- 机器学习 (ML) 为准确的HSP预测提供了一个有希望的方法.
- 优化共晶形成需要可靠的可溶性数据.
研究的目的:
- 开发和评估一个使用 ML 预测 HSP 的计算框架.
- 为了比较内核回归 (KRR),多线性回归 (MLR) 和直角匹配追求 (OMP) 模型的HSP预测性能.
- 为了确定最有效的ML模型用于制药晶配方.
主要方法:
- 开发了一个利用ML算法的计算框架:KRR,MLR和OMP.
- 在预测三个关键的汉森溶解度参数方面优化模型性能.
- 使用诸如根平均平方误差 (RMSE),R2,平均绝对误差 (MAE) 和蒙特卡洛交叉验证 (CV) 等指标评估模型有效性.
- 采用 Tabu Search 方法进行严格的模型优化.
主要成果:
- 与MLR和OMP相比,Kernel Ridge回归 (KRR) 在预测汉森溶解度参数方面表现优越.
- 开发的框架为制药配方中HSP预测提供了一个强大的方法.
- 对比分析强调了基于特定输出要求的算法选择的重要性.
结论:
- 该KRR模型是高效的预测汉森溶解度参数在制药的共同晶体的发展.
- 这项研究为选择适当的ML模型用于药物设计中的回归任务提供了有价值的见解.
- 这些发现支持药物的合理设计和有效查可溶性增强的辅制剂.
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