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通过计算推进制药智能 通过机器学习模型预测快速分解片的体外参数
Dhruv Gupta1, Anuj A Biswas1, Rohan Chand Sahu1
1Department of Pharmaceutical Engineering and Technology, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh, India.
机器学习模型预测快速溶解的平板电脑特性,如分解时间,易碎性和吸水性. 这些人工智能工具加速药物开发,降低成本和实验代.
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:
- 机器学习 (ML) 越来越多地用于药物开发.
- 药片的有效性取决于物理化学性质,配方和加工.
- 预测药片特征有助于优化药物输送.
研究的目的:
- 开发ML模型来预测快溶片的分解时间,易碎性和吸水率.
- 使用RMSE和R平方指标评估模型性能.
- 提供一种新的方法来预测平板电脑的性能.
主要方法:
- 数据可视化,预处理和分割.
- 创建和评估ML模型,包括投票回归器,随机森林和KNN.
- 使用了超参数调整和交叉验证.
主要成果:
- 投票回归器实现了最佳的分解时间预测 (RMSE:21.99,R2:0.76).
- 随机森林回归器在脆性预测方面表现出色 (RMSE:0.142,R2:0.7).
- KNN回归器在吸水比率方面表现出卓越的性能 (RMSE: 10.07,R2: 0.94).
结论:
- 机器学习模型可以准确地预测关键的平板电脑特性.
- 这种方法提供了显著的进步,特别是在可碎性和吸水率预测方面.
- 开发的模型可以简化平板电脑开发,减少时间和资源.
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