Superior Performance of Extreme Gradient Boosting Model Combined with Affinity Propagation Clustering for Reliable Prediction of Permissible Exposure Limits of Hydrocarbons and Their Oxygen-Containing Derivatives

In order to conveniently and efficiently determine the Permissible Exposure Limits (PELs) of organic chemicals in the workplace, this study employed Quantitative Structure–Activity Relationship (QSAR) modeling to predict properties related to occupational health and safety. The predictive study was conducted by correlating the PELs of 75 hydrocarbons and their oxygen-containing derivatives with the molecular structures of the organic compounds. Meanwhile, this study conducted a comprehensive and in-depth comparative analysis of the four developed predictive models. The sample set was partitioned using the Affinity Propagation (AP) clustering algorithm. Four characteristic molecular descriptors were selected by integrating the Genetic Algorithm (GA) with the variance inflation factor (VIF) value. Subsequently, the Multiple Linear Regression (MLR) model and two nonlinear models, namely the Support Vector Machine (SVM) and the Extreme Gradient Boosting (XGBoost), were developed and used for predictive comparison. Furthermore, the performance of the models was evaluated through both internal and external validation methods, and the Williams plots were constructed to define the model’s applicability domain. The results indicated that the XGBoost model achieved high performance, with a coefficient of determination (R2) of 0.9962 on the training set and 0.8892 on the testing set. The corresponding root mean square errors (RMSE) were 0.1012 and 0.6623 for the training and testing sets, respectively. The internal validation coefficient (Q2loo) was 0.8975, while the external validation coefficient (Q2ext) was 0.832. Moreover, the majority of the sample data (approximately 96%) fell within the application domain defined by ±3 times the standard residue-to-critical arm ratio, where h* = 0.2. This demonstrates that the XGBoost model exhibits excellent fitting capability, stability, and predictive power, thereby uncovering a significant nonlinear relationship between the molecular structure of compounds and the PELs. As outlined above, the utilization of the QSAR method for predicting the PELs of hydrocarbons and their oxygen-containing derivatives constitutes a highly effective approach.