Molreac-Oxi: An end-to-end deep learning-quantum chemistry platform for •OH reactivity (k_OH), pathways, and...

To address the long-standing challenge of efficiently evaluating reaction rate constants (k_OH) for pollutant-hydroxyl radical (•OH) systems in environmental pollution control, a hybrid meta-model framework is introduced that fuses deep pretrained models with traditional machine learning, together with an integrated platform that unifies prediction, mechanistic inference, and online analysis. From DFT-optimized structures, multidimensional quantum-chemical descriptors were extracted for 968 pollutants, and a large-scale pretrained 3D molecular model (Uni-Mol) was fine-tuned. The fine-tuned Uni-Mol model was stacked alongside first-layer learners-Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost-whose outputs were fused by a regularized linear meta-learner to estimate k_OH. The stacked-ensemble attains R² = 0.806 with a lower MAE than any single learner, and parity plots and residual diagnostics for log₁₀(k_OH) indicate limited bias across major chemical classes. Interpretability is enhanced with SHAP (SHapley Additive exPlanations) and conditional, correlation-aware effect estimates; where appropriate, bootstrap-supported thresholds are reported to avoid over-interpreting collinear descriptors. To compensate for the limited PES (potential-energy-surface) resolution of static structure-property models, a PES-Learn model trained on 72,502 organic pollutants is coupled to a nanoreactor MD workflow so that mechanism-level hypotheses can be generated at near-DFT fidelity and orders-of-magnitude lower cost; on a GPU, inference achieves speedups of up to ∼3.1 × 10⁴ over conventional DFT. These models and CDFT analysis are encapsulated in an online platform (https://www.bohrium.com/apps/molreac-oxi), providing a closed-loop workflow from rapid batch screening to reaction-pathway and active-site analysis.