开发了几种基于机器学习的模型,用于在不同温度下在二元溶剂中确定小分子制药溶解度
Mohammed Alqarni1, Ali Alqarni2
1Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University, P.O. Box 11099, 21944, Taif, Saudi Arabia. m.aalqarni@tu.edu.sa.
Scientific reports
|August 7, 2025
概括
先进的机器学习模型准确地预测混合溶剂中的药物溶解度. 基于贝叶斯的神经网络 (BNN) 和神经忘记决策集团 (NODE) 显示高精度,优化结晶过程.
科学领域:
- 计算化学计算化学
- 制药科学 制药科学
- 机器学习应用 机器学习应用
背景情况:
- 在二元溶剂混合物中的药物溶解性对于制药开发和结晶至关重要.
- 在复杂的非线性系统中预测可溶性行为需要先进的建模技术.
- 研究了在不同温度和度下里瓦罗克萨班在二甲和初级酒精中的溶解性.
研究的目的:
- 用机器学习分析和预测里瓦罗克萨班在二元溶剂系统中的溶解度.
- 为了比较多项式曲线拟合,贝叶斯式神经网络 (BNN) 和神经遗忘决策合集 (NODE) 模型的性能.
- 为了优化模型的超参数,使用随机分数搜索 (SFS) 算法.
主要方法:
- 应用三种回归模型:多项式曲线拟合,贝叶斯式神经网络 (BNN) 和神经遗忘决策组合 (NODE).
- 机器学习模型的超参数优化,使用静态分数搜索 (SFS) 算法.
- 使用R2,平均平方误差 (MSE) 和平均绝对百分比误差 (MAPE) 评估模型性能.
主要成果:
- 基于贝叶斯神经网络 (BNN) 的模型表现出卓越的性能,测试R2为0.9926和MSE为3.07×10−8.
- 神经遗忘决策组合 (NODE) 模型实现了0.9413的测试R2和0.1835.3的最低MAPE.
- 多项式曲线拟合显示了局限性,产生了0.8200的较低测试R2和更高的错误率.
结论:
- 先进的机器学习模型,特别是BNN和NODE,对于预测二元溶剂混合物的药物溶解度非常有效.
- 这些模型为改善结晶过程的设计和优化提供了巨大的潜力.
- 这些发现突出了复杂的计算方法的能力,以解开复杂的溶解性关系.
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