COMPUTATIONAL DESIGN OF GREEN ORGANIC SYNTHESIS ROUTES USING MACHINE LEARNING AND GFN-XTB TECHNIQUES

Abstract

Traditional organic synthesis often relies on hazardous reagents and energy-intensive processes, posing significant environmental and health risks. While green chemistry principles advocate for sustainable alternatives, the systematic design of eco-friendly synthetic routes remains challenging due to the vast chemical space and the lack of computational tools integrating both predictive and mechanistic insights. This study addressed this gap by developing a hybrid machine learning (ML) and quantum mechanical (GFN-xTB) framework to identify optimal green synthesis pathways. The primary objective was to predict reaction efficiency while quantifying sustainability metrics, including atom economy and E-factor. A curated dataset of 1,500 reactions (substitution, addition, elimination, redox) was analyzed using XGBoost, Random Forest, and Support Vector Regression, with input features derived from structural fingerprints, reaction conditions, and GFN-xTB-computed properties (ΔG, HOMO-LUMO gap). Statistical analyses included multiple linear regression (MLR), ANOVA, and SHAP interpretability. Key findings revealed that addition reactions exhibited the highest yields (6.7–12.8% greater than other classes, *p* < 0.001) and alignment with green criteria. The best-performing model (XGBoost, R² = 0.92, MAE = 3.5%) identified ΔG and HOMO-LUMO gap as dominant predictors, with green reactions demonstrating superior yields (83.1% vs. 72.9%, *p* < 0.001) and lower E-factors (2.2 vs. 5.8). These results establish a robust computational strategy for sustainable reaction design, bridging data-driven prediction with quantum-chemical validation. The study provides a scalable tool for reducing experimental trial-and-error, with implications for pharmaceutical and industrial chemistry. By prioritizing both efficiency and environmental impact, this work advances the integration of AI and quantum methods in green chemistry.