AI-Guided Design of a Self-Healing Polymer-Supported Organocatalyst for Sustainable Multicomponent Synthesis of Bioactive Heterocycles

Introduction

The development of recyclable and environmentally sustainable catalysts remains a major challenge in organic synthesis. Polymer-supported organocatalysts offer advantages including catalyst recovery, reduced waste generation, and enhanced operational stability. However, catalyst design and reaction optimization often require extensive experimental screening. This study presents an artificial intelligence-assisted strategy for developing a self-healing polymer-supported organocatalyst for multicomponent synthesis of pharmaceutically relevant heterocyclic compounds.

Methods: A dynamic covalent polymer network incorporating imidazole-based catalytic moieties was designed using machine-learning-assisted molecular optimization. Random Forest and Bayesian Optimization models were trained using catalyst descriptors, reaction yields, turnover frequencies, and catalyst stability data extracted from published studies. The optimized catalyst architecture was evaluated computationally for catalytic activity in the one-pot synthesis of substituted dihydropyrimidinones via the Biginelli reaction. Polymer stability, active-site accessibility, and catalyst regeneration behavior were predicted using molecular simulations.

Results: The optimized catalyst demonstrated a predicted turnover frequency of 412 h⁻¹, representing a 36% improvement over conventional polymer-supported analogues. Machine-learning analysis identified catalyst flexibility and active-site density as the most influential parameters governing reaction efficiency. The optimized system achieved a predicted product yield of 94.8 ± 1.6% under mild conditions. The dynamic polymer framework retained 91.5% catalytic activity after five regeneration cycles and exhibited self-healing efficiencies exceeding 95% following mechanical disruption. Bayesian optimization reduced experimental screening requirements by approximately 82% compared with traditional catalyst development approaches.

Conclusions: The integration of artificial intelligence with dynamic polymer catalysis provides a powerful platform for accelerating catalyst discovery and reaction optimization. The proposed methodology demonstrates the potential of self-healing polymer-supported organocatalysts for sustainable synthesis of bioactive heterocycles and establishes a framework for future AI-driven catalyst engineering.