Artificial Intelligence-Driven Optimisation of Suzuki–Miyaura Cross-Coupling Reactions: Integrating the AI-GCO Framework for Sustainable and Green Chemical Synthesis

Abstract

The Suzuki–Miyaura cross-coupling reaction is among the most widely employed carbon–carbon bond-forming reactions in synthetic organic chemistry. Despite its broad utility, conventional protocols rely on hazardous solvents such as toluene and dichloromethane, stoichiometric inorganic bases, and energy-intensive heating all of which conflict with the Twelve Principles of Green Chemistry articulated by Anastas and Warner (1998). This research paper investigates how Artificial Intelligence (AI) and Machine Learning (ML) can be systematically applied to render this reaction sustainable, efficient, and scalable. A four-stage AI-Green Chemistry Optimisation (AI-GCO) Framework is proposed, integrating Molecular Transformer networks for retrosynthetic planning, COSMO-RS with Random Forest classification for green solvent screening, Life Cycle Assessment (LCA)-coupled multi-objective route ranking, and hybrid ML-Computational Fluid Dynamics (CFD) scale-up simulation. The framework is validated on the model reaction of 4-bromoanisole with phenylboronic acid to yield 4-methoxybiphenyl. Key outcomes include a predicted yield of 89.4 ± 2.1%, E-factor of 6.2 ± 0.8 kg waste/kg product (reduced from 32.1 in the toluene baseline), Global Warming Potential (GWP) of 4.1 ± 0.6 kg CO2-eq/kg product, and Process Mass Intensity (PMI) of 18.4 ± 1.2 representing a 43% improvement over the conventional protocol. The industrial sitagliptin biocatalytic case study further demonstrates an 86% reduction in E-factor (50.3 ± 6.1 to 7.1 ± 1.2, p < 0.001) alongside a 104% yield gain. A meta-analysis of 72 peer-reviewed studies (2018–2025) provides statistical grounding for task-specific model selection, revealing that Transformer networks, Bayesian Optimisation, and Random Forest classifiers each excel within distinct sub-problems of the green chemistry workflow. Taken together, these findings underscore the transformative potential of AI as a practical enabling tool for green synthesis, offering chemists a rigorous, data-driven alternative to exhaustive experimental screening in both academic research and industrial manufacturing contexts. All computational findings are reported as mean ± standard deviation from five independent runs, with statistical significance confirmed at α = 0.05. Important disclaimer: all yield, E-factor, PMI, and GWP values reported in this study are computational predictions derived from machine learning models and literature data, not experimental measurements. Future experimental validation at laboratory and pilot scale is explicitly recommended to confirm these predicted outcomes.