Molecular Generation and Performance Optimization of Environmentally Friendly Organic Reaction Catalysts Based on Conditional Diffusion Models

The design of environmentally friendly organic reaction catalysts with high activity and selectivity remains a core challenge in green chemistry, as traditional trial-and-error methods suffer from low efficiency and limited chemical space exploration. Conditional diffusion models (CDMs), emerging as powerful generative AI tools, offer unprecedented capabilities in molecular design by leveraging conditional constraints to guide targeted generation. Herein, we propose a CDM-based framework for the rational design and performance optimization of green organic catalysts, focusing on two representative reactions: asymmetric Henry reaction and CO_2 cycloaddition. First, a multi-modal conditional module integrating molecular descriptors (e.g., HOMO-LUMO gap, charge distribution) and reaction-specific requirements (e.g., enantioselectivity, catalytic activity) is constructed to embed conditional information into the diffusion process. Second, a hybrid loss function combining denoising score matching (DSM) and property prediction loss is developed to enhance the alignment between generated molecules and target properties. Finally, a high-throughput virtual screening (HTVS) pipeline integrated with quantum mechanical (QM) calculations is established to validate the catalytic performance of generated candidates. Experimental results demonstrate that the proposed CDM framework outperforms state-of-the-art generative models in terms of generation diversity ( 92.3% unique molecules), property alignment ( 87.1% of generated molecules meet target property thresholds), and catalytic activity (12 out of 20 synthesized candidates exhibit catalytic efficiency higher than commercial catalysts). Notably, the best candidate for CO_2 cycloaddition achieves a turnover frequency (TOF) of 420h^−1 and a selectivity of 99.5% under mild conditions ( 30°C, 1 atmCO_2 ), while the optimal asymmetric Henry catalyst delivers an enantiomeric excess (ee) of 96.2%. This work provides a paradigm for the rapid development of environmentally friendly catalysts via AI-driven generative design, bridging the gap between computational modeling and experimental catalysis.