Yunwu Fan, Yu Deng, Yi Yang, Xin Deng, Qianhui Li, Boqi Xu, Jianyu Pan, Sisi Liu, Yan Kong and Chang-Er Chen
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The QSRR models were developed using the no-code machine learning tool, Orange, employing simple 2D molecular descriptors as input features. Through a systematic analysis, 12 descriptors were identified as pivotal properties essential for developing optimal models (including multiple linear regression – MLR and support vector machine – SVM). These selected models demonstrate great internal validation metrics (<em>R</em><small><sup>2</sup></small> > 0.98, MAE < 6.5 s) and reasonable external robustness (<em>R</em><small><sup>2</sup></small> > 0.80, MAE ∼ 40 s). Furthermore, a concise model interpretation was conducted to elucidate the molecular factors influencing LC-RT. It is anticipated that our models, capable of predicting the LC-RT for over 2000 PFAS within the Norman Network, will be instrumental in addressing this environmental challenge. 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引用次数: 0
摘要
由于机器学习在解决各种环境问题方面的潜力,它在环境科学中越来越受欢迎和有前途。其中一个全球性问题是持久性化学品-全氟烷基和多氟烷基物质(PFAS)造成的污染,威胁着环境和人类。在这里,我们引入了一种无代码机器学习方法来模拟PFAS的液相色谱保留时间(LC-RT)的定量结构-保留关系(QSRR)。这种方法旨在简化建模过程,特别是对于可能发现密集编码很麻烦的环境专业人员。QSRR模型是使用无代码机器学习工具Orange开发的,采用简单的二维分子描述符作为输入特征。通过系统分析,确定了12个描述符作为开发最优模型(包括多元线性回归- MLR和支持向量机- SVM)所必需的关键属性。这些选定的模型展示了良好的内部验证度量(R2 >0.98, MAE <6.5 s)和合理的外部稳健性(R2 >0.80, MAE ~ 40 s)。此外,进行了简明的模型解释,以阐明影响LC-RT的分子因素。预计我们的模型能够预测诺曼网络内超过2000个PFAS的LC-RT,将有助于解决这一环境挑战。这项研究不仅为PFAS LC行为提供了有价值的见解,而且还为未来无代码机器学习模型的开发和应用提供了催化剂。
Modelling and predicting liquid chromatography retention time for PFAS with no-code machine learning†
Machine learning is increasingly popular and promising in environmental science due to its potential in solving various environmental problems. One such worldwide issue is the pollution caused by the persistent chemicals – per- and polyfluoroalkyl substances (PFAS), threatening the environment and human beings. Here, we introduce a no-code machine learning approach for modelling the quantitative structure–retention relationship (QSRR) of liquid chromatographic retention time (LC-RT) for PFAS. This approach aims to streamline the modelling process, particularly for environmental professionals who may find intensive coding cumbersome. The QSRR models were developed using the no-code machine learning tool, Orange, employing simple 2D molecular descriptors as input features. Through a systematic analysis, 12 descriptors were identified as pivotal properties essential for developing optimal models (including multiple linear regression – MLR and support vector machine – SVM). These selected models demonstrate great internal validation metrics (R2 > 0.98, MAE < 6.5 s) and reasonable external robustness (R2 > 0.80, MAE ∼ 40 s). Furthermore, a concise model interpretation was conducted to elucidate the molecular factors influencing LC-RT. It is anticipated that our models, capable of predicting the LC-RT for over 2000 PFAS within the Norman Network, will be instrumental in addressing this environmental challenge. This study not only contributes valuable insights into PFAS LC behaviour but also serves as a catalyst for future endeavours in the development and applications of no-code machine learning models.