基于分子结构的机器学习模型直接预测土壤中植物根系中有机污染物的生物累积,Environmental Science & Technology

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基于分子结构的机器学习模型直接预测土壤中植物根系中有机污染物的生物累积
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2021-12-03 , DOI: 10.1021/acs.est.1c02376
Feng Gao ₁ , Yike Shen ₂ , Jonathan Brett Sallach ₃ , Hui Li ₄ , Cun Liu ₅ , Yuanbo Li ₆

Affiliation

根浓度因子 (RCF) 是描述植物在生命周期影响评估 (LCIA) 和植物修复潜力评估中土壤中有机污染物积累的重要表征参数。然而，由于化学-土壤-植物根系之间的复杂相互作用，建立稳健的预测模型仍然具有挑战性。在这里，我们开发了端到端机器学习模型，通过在包含 341 个数据点的统一 RCF 数据集上进行训练，将复杂的分子结构与 RCF 关系转移，涵盖 72 种化学品。我们通过预测 RCF 值证明了所提出的基于扩展连接指纹 (ECFP) 的梯度提升回归树 (GBRT) 模型的有效性，并使用 5- 实现了 R 平方为 0.77 和平均绝对误差 (MAE) 为 0.22 的预测性能。折叠交叉验证。此外，我们的结果揭示了化学、土壤和植物特性之间的非线性关系。进一步深入分析确定了与 RCF 相关的关键化学拓扑子结构（例如，-O、-Cl、芳环和大型共轭 π 系统）。由于其简单性和普遍性，GBRT-ECFP 模型为 LCIA 和其他环境评估提供了一个有价值的工具，以更好地描述化学对人类健康和生态系统的风险。

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Direct Prediction of Bioaccumulation of Organic Contaminants in Plant Roots from Soils with Machine Learning Models Based on Molecular Structures

Root concentration factor (RCF) is an important characterization parameter to describe accumulation of organic contaminants in plants from soils in life cycle impact assessment (LCIA) and phytoremediation potential assessment. However, building robust predictive models remains challenging due to the complex interactions among chemical–soil–plant root systems. Here we developed end-to-end machine learning models to devolve the complex molecular structure relationship with RCF by training on a unified RCF data set with 341 data points covering 72 chemicals. We demonstrate the efficacy of the proposed gradient boosting regression tree (GBRT) model based on the extended connectivity fingerprints (ECFP) by predicting RCF values and achieved prediction performance with R-squared of 0.77 and mean absolute error (MAE) of 0.22 using 5-fold cross validation. In addition, our results reveal nonlinear relationships among properties of chemical, soil, and plant. Further in-depth analyses identify the key chemical topological substructures (e.g., −O, −Cl, aromatic rings and large conjugated π systems) related to RCF. Stemming from its simplicity and universality, the GBRT-ECFP model provides a valuable tool for LCIA and other environmental assessments to better characterize chemical risks to human health and ecosystems.

更新日期：2021-12-21

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