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.

The scarcity of reliably determined STELs for numerous chemicals severely impedes occupational health risk assessment. To address this gap, this study establishes and validates a suite of robust quantitative structure–activity relationship (QSAR) models to efficiently predict STELs for hydrocarbons and their derivatives. A dataset of 60 compounds was partitioned using Affinity Propagation clustering, and the validity of this division was verified using Tanimoto similarity analysis and Uniform Manifold Approximation and Projection (UMAP). Four optimal molecular descriptors, indicative of molecular size and spatial configuration, were identified using a genetic algorithm. These descriptors served as inputs for one linear model—multiple linear regression (MLR)—and three nonlinear models: support vector machine (SVM), back-propagation artificial neural network (BP-ANN), and extreme gradient boosting (XGBoost). All models were rigorously validated according to OECD principles. The results demonstrated that the XGBoost model achieved superior performance, with key metrics ($R^2$, $Q_{\mathrm{loo}}^2$, $Q_{\mathrm{ext}}^2$) all exceeding 0.9. Interpretability analysis using SHAP (SHapley Additive exPlanations) revealed that molecular size and symmetry descriptors (E3u, G2m) positively correlate with STEL, while the degree of unsaturation (n = CHR) shows a significant negative influence, providing novel mechanistic insights into the structure–toxicity relationship. Notably, 96% of the predictions fell within the defined applicability domain, confirming the model’s reliability. This study therefore serves as a rapid, accurate, interpretable, and reliable computational tool, with the potential to significantly inform and enhance occupational health and safety decision-making, especially for novel or data-poor chemicals.