A High-Performance and Interpretable pKa Prediction Framework Integrating Count-Based Fingerprints and Ensemble Learning

The acid dissociation constant (p<i>K</i>_a) is a fundamental parameter governing the environmental fate of organic compounds. Accurate p<i>K</i>_a prediction remains challenging, as traditional binary Morgan fingerprints (B-MF) fail to capture stoichiometric information critical for modeling substituent effects. This study developed an interpretable machine learning framework for p<i>K</i>_a prediction by integrating count-based Morgan fingerprints (C-MF) with ensemble algorithms. Through systematic comparison across four algorithms (Catboost, XGBoost, GBDT, RF), C-MF consistently outperformed B-MF due to its ability to quantify functional group multiplicity. Subsequent SHAP-based recursive feature elimination (SHAP-RFE) optimized the model, identifying Catboost with only 81 features as the optimal architecture, achieving a test-set <i>R</i>² of 0.890 and <i>RMSE</i> of 1.026. SHAP analysis revealed that the model's decisions are driven by chemically intuitive features, forming a hierarchical framework where primary ionizable sites set the baseline p<i>K</i>_a and electronic modifiers fine-tune it. The applicability domain, defined using the AD_SAL method, yielded high-confidence predictions (<i>R</i>² = 0.926). External validation on an independent open-source dataset containing 6876 acidic compounds, combined with results from AD_SAL application domain characterization, enabled accurate p<i>K</i>_a prediction for 390 compounds within the application domain (<i>R</i>² = 0.890, <i>RMSE</i> = 0.942). This further confirms the model's strong generalizability. This work provides a robust and generalizable tool for high-performance p<i>K</i>_a prediction, with significant potential for applications in environmental risk assessment.