On February 1, 2021, Zhong, Shifa; Zhang, Kai; Wang, Dong; Zhang, Huichun published an article.Computed Properties of 111-29-5 The title of the article was Shedding light on “Black Box” machine learning models for predicting the reactivity of HO路 radicals toward organic compounds. And the article contained the following:
Developing quant. structure-activity relationships (QSARs) is an important approach to predicting the reactivity of HO radicals toward newly emerged organic compounds However, it is yet unknown whether this method makes predictions by choosing meaningful structural features rather than spurious ones, which is vital for trusting the models. In this study, we developed QSAR models for the logkHO路 values of 1089 organic compounds in the aqueous phase by two ML algorithms-deep neural networks (DNN) and eXtreme Gradient Boosting (XGBoost), and interpreted the built models by the SHapley Additive exPlanations (SHAP) method. The results showed that for the contribution of a given structural feature to logkHO. for different compounds, DNN and XGBoost treated it as a fixed and variable value, resp. We then developed an ensemble model combining the DNN with XGBoost, which achieved satisfactory predictive performance for all three datasets: Training dataset: R-square (R2) 0.89-0.91, root-mean-squared-error (RMSE) 0.21-0.23, and mean absolute error (MAE) 0.15-0.17; Validation dataset: R2 0.63-0.78, RMSE 0.29-0.32, and MAE 0.21-0.25; and Test dataset: R2 0.60-0.71, RMSE 0.30-0.35, and MAE 0.23-0.25. This study offered some much-needed mechanistic insights into a ML-assisted environmental task, which are important for evaluating the trustworthiness of the ML-based models, further improving the models for specific applications, and leveraging the implicit knowledge the models carry. The experimental process involved the reaction of Pentane-1,5-diol(cas: 111-29-5).Computed Properties of 111-29-5
The Article related to black box machine learning model radical organic compound, Water: Analysis and other aspects.Computed Properties of 111-29-5
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