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Updated: Jan 9, 2026

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM
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A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

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开发高斯过程回归,拉索回归和努支持向量回归模型,用于预测在超临界CO2中埃克塞梅斯坦的可溶性

Jawza A Almutairi1, Thamir Malik2,3

  • 1Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia. Jaalmutairi@pnu.edu.sa.

Scientific reports
|December 8, 2025
PubMed
概括

高斯过程回归准确地预测药物在超临界二氧化碳 (scCO2) 中的溶解性,优于其他机器学习模型. 这一进步有助于优化制药配方和药物输送系统.

关键词:
药物溶解性 药物溶解性 药物溶解性这里是Exemestane.高斯过程回归的高斯过程回归.拉索回归是一种回归方式.机器学习 机器学习这是一个Nu-Support向量回归.制药加工 制药加工 制药加工超临界的二氧化碳二氧化碳

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