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

To address the long-standing challenge of efficiently evaluating reaction rate constants (k) 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. The stacked-ensemble attains R = 0.806 with a lower MAE than any single learner, and parity plots and residual diagnostics for log(k) 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 × 10 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.