基于机器学习的预测印记质量使用整体和非线性回归算法
Bita Yarahmadi1, Seyed Majid Hashemianzadeh2, Seyed Mohammad-Reza Milani Hosseini1
1Real Samples Analysis Laboratory, Department of Chemistry, Iran University of Science and Technology, Tehran, Iran.
机器学习准确地预测了分子印记聚合物 (MIP) 的印记因子 (IF). 梯度增强模型提供了一种更快,更有效的方法来优化MIP合成并提高选择性.
科学领域:
- 聚合物化学 聚合物化学
- 材料科学 材料科学 材料科学
- 计算化学的计算化学
背景情况:
- 分子印记聚合物 (MIP) 是多功能的人造材料,具有定制的识别点.
- MIP提供稳定性,可重复使用性和高选择性,但优化合成条件是具有挑战性的.
- 目前的优化方法是耗时的,昂贵的,资源密集的.
研究的目的:
- 研究机器学习 (ML) 的应用,以预测MIPs的印记因子 (IF).
- 克服MIP合成传统实验优化的局限性.
- 确定影响MIP选择性和性能的关键因素.
主要方法:
- 使用的非线性回归ML算法:分类和回归树,支持向量回归,k-最近邻居和整体方法 (梯度增强,随机森林,额外树木).
- 采用相互信息特征选择方法来确定影响IF的关键参数.
- 经过训练的ML模型使用实验衍生数据,包括pH,模板类型,单体类型,溶剂,KMIP和KNIP.
主要成果:
- 梯度提升 (GB) 算法在预测IF方面表现出卓越的性能.
- GB实现了最高的R平方值 (0.871),表明了强大的模型匹配.
- GB还产生了最低的平均绝对误差 (MAE = -0.982) 和平均平方误差 (MSE = -2.303).
结论:
- 机器学习,特别是梯度增强,是预测MIP打印因素的强大工具.
- 基于ML的预测显著加快了MIP合成的优化,减少了实验成本和时间.
- 这种方法提高了开发选择性分子印记聚合物的效率和准确性.
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