使用热力学和相互作用机制通过模型比较和验证对药物溶解性的计算智能分析
Ahmad J Obaidullah1, Wael A Mahdi2
1Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, 11451, Riyadh, Saudi Arabia.
Scientific reports
|November 28, 2024
概括
高斯过程回归 (GPR) 在预测药物溶解性和聚合物相互作用方面表现出色. 这种强大的模型,通过烟花算法 (FWA) 进行优化,为制药研究中的复杂数据集提供了精确的预测.
科学领域:
- 制药科学 制药科学
- 计算化学计算化学
- 数据科学数据科学数据科学
背景情况:
- 准确预测药物溶解度和API-聚合物相互作用对于药物开发至关重要.
- 复杂的数据集对传统的预测建模技术构成挑战.
研究的目的:
- 评估和比较各种回归模型的性能,以预测药物溶解性和API-聚合物相互作用.
- 在这些模型中评估烟花算法 (FWA) 对超参数调整的有效性.
主要方法:
- 研究了高斯过程回归 (GPR),支向量回归 (SVR),贝叶斯山脊回归 (BRR) 和内核山脊回归 (KRR).
- 应用Z-score预处理用于异常值检测和数据精制.
- 使用烟花算法 (FWA) 进行超参数优化.
主要成果:
- 高斯过程回归 (GPR) 显示出卓越的性能,达到最高的R平方值 (训练为0.9980,测试为0.9950) 和最低的平均平方误差 (MSE) 和平均绝对误差 (MAE).
- 烟花算法 (FWA) 有效地提高了回归模型的预测能力.
- Z-score预处理提高了数据准确性和分析可靠性.
结论:
- 高斯过程回归 (GPR) 是用于制药和科学领域复杂回归任务的强大而精确的方法.
- 烟花算法 (FWA) 显示了优化预测模型的巨大潜力.
- 选择适当的回归模型和优化技术对于可靠的预测分析至关重要.
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