计算智能调查评估不同温度下不同溶剂中的酸可溶性
Adel Alhowyan1, Wael A Mahdi2, Ahmad J Obaidullah3
1Department of Pharmaceutics, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh, 11451, Saudi Arabia.
Scientific reports
|February 28, 2025
概括
额外的树木机器学习模型准确地预测了酸在13种溶剂中的溶解度. 这种组合方法在制药制造应用中表现出卓越的性能.
科学领域:
- 计算化学计算化学
- 机器学习在药物发现中的作用
背景情况:
- 准确预测药物溶解度对于制药开发至关重要.
- 机器学习为模拟复杂化学性质提供了强大的工具.
研究的目的:
- 评估基于树的机器学习算法,用于预测酸溶解度.
- 为了确定与压力,温度和溶剂成分相关的溶解度的最佳性能模型.
主要方法:
- 使用立体主义,梯度增强 (GB),极端梯度增强 (XGB) 和额外树木 (ET) 模型.
- 使用标准缩量器预处理的数据和使用库克距离检测到的异常值.
- 使用差异演变 (DE) 优化超参数和使用蒙特卡洛交叉验证评估模型.
主要成果:
- 额外树木 (ET) 模型实现了高的R平方值0.996.6.
- 与其他测试模型相比,ET显示出更高的准确性和一致性.
- 使用R平方,根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 来测量性能.
结论:
- 组合方法,特别是额外树,对于预测药物溶解度是有效的.
- 这些模型在制药制造中捕获复杂数据模式时表现出弹性.
- 该研究强调了机器学习在优化药物开发过程中的潜力.
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