基于机器学习的分析药物化合物与聚合物的相互作用,以估计配方中的药物溶解度
Ahmad J Obaidullah1, Wael A Mahdi2
1Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, P.O. Box 2457, Riyadh, 11451, Saudi Arabia. aobaidullah@ksu.edu.sa.
Scientific reports
|July 2, 2025
概括
这项研究开发了一种机器学习框架,以准确预测药物的溶解性和活性. 与特征选择和超参数优化相结合的学习显著提高了药物配方的预测准确性.
科学领域:
- 计算化学和化学信息学
- 机器学习在药物发现中的应用.
- 制药配方的开发和发展
背景情况:
- 准确预测药物的溶解性和活性对于有效的制药配方至关重要.
- 确定这些属性的传统方法可能是耗时和资源密集的.
- 机器学习提供了一种有希望的方法来加速和提高预测准确性.
研究的目的:
- 引入一种复杂的机器学习框架,用于预测配方中的药物溶解度和活性.
- 利用集合学习技术来提高预测准确度.
- 通过高级功能选择和超参数调整来优化预测模型.
主要方法:
- 使用了一个包含超过12,000行数据和24个输入特征的综合数据集.
- 通过使用AdaBoost改进基础模型 (决策树,K-最近邻居,多层感知器) 实现集体学习.
- 实现了用于特征选择的递归特征消除和用于超参数优化的和搜索.
主要成果:
- 在药物溶解性预测方面,ADA-DT模型获得了0.9738的R2得分.
- ADA-KNN模型在马预测方面获得了0.9545的R2值.
- 这两种模型都表现出高精度,平均平方误差 (MSE) 和平均绝对误差 (MAE) 较低.
结论:
- 合体学习,结合高级特征选择和超参数优化,准确地预测复杂的生物化学性质.
- 开发的框架为加强药物配方开发提供了一个强大的工具.
- 这种方法可以显著加速药物发现和开发管道.
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