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

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.

Suzuki–Miyaura coupling; Green Chemistry

1. Ahneman, D.T., Estrada, J.G., Lin, S., Dreher, S.D. and Doyle, A.G. (2018) 'Predicting reaction performance in C–N cross-coupling using machine learning', Science, 360(6385), pp. 186–190. doi: 10.1126/science.aar5169. [Google Scholar] [Crossref]

2. Anastas, P.T. and Warner, J.C. (1998) Green Chemistry: Theory and Practice. Oxford: Oxford University Press. [Google Scholar] [Crossref]

3. Byrne, F.P. et al. (2016) 'Tools and techniques for solvent selection: green solvent selection guides', Sustainable Chemical Processes, 4(1), pp. 1–24. doi: 10.1186/s40508-016-0051-z. [Google Scholar] [Crossref]

4. Coley, C.W., Green, W.H. and Jensen, K.F. (2018) 'Machine learning in computer-aided synthesis planning', Accounts of Chemical Research, 51(5), pp. 1281–1289. doi: 10.1021/acs.accounts.8b00087. [Google Scholar] [Crossref]

5. Coley, C.W. et al. (2020) 'A robotic platform for flow synthesis of organic compounds informed by AI', Science, 365(6453), eaax1566. [Google Scholar] [Crossref]

6. Dunn, P.J. (2012) 'The importance of green chemistry in process research and development', Chemical Society Reviews, 41(4), pp. 1452–1461. [Google Scholar] [Crossref]

7. Gao, H. et al. (2018) 'Using machine learning to predict suitable conditions for organic reactions', ACS Central Science, 4(11), pp. 1465–1476. [Google Scholar] [Crossref]

8. Huffman, M.A. et al. (2019) 'Design of an in vitro biocatalytic cascade for the manufacture of islatravir', Science, 366(6470), pp. 1255–1259. [Google Scholar] [Crossref]

9. Jimenez-Luna, J., Grisoni, F. and Schneider, G. (2020) 'Drug discovery with explainable artificial intelligence', Nature Machine Intelligence, 2(10), pp. 573–584. [Google Scholar] [Crossref]

10. Page, M.J. et al. (2021) 'The PRISMA 2020 statement: an updated guideline for reporting systematic reviews', BMJ, 372, n71. [Google Scholar] [Crossref]

11. Plutschack, M.B. et al. (2017) 'The hitchhiker's guide to flow chemistry', Chemical Reviews, 117(18), pp. 11796–11893. [Google Scholar] [Crossref]

12. Savile, C.K. et al. (2010) 'Biocatalytic asymmetric synthesis of chiral amines from ketones applied to sitagliptin manufacture', Science, 329(5989), pp. 305–309. [Google Scholar] [Crossref]

13. Schwaller, P. et al. (2019) 'Molecular Transformer: a model for uncertainty-calibrated chemical reaction prediction', ACS Central Science, 5(9), pp. 1572–1583. [Google Scholar] [Crossref]

14. Segler, M.H., Preuss, M. and Waller, M.P. (2018) 'Planning chemical syntheses with deep neural networks and symbolic AI', Nature, 555(7698), pp. 604–610. [Google Scholar] [Crossref]

15. Sheldon, R.A. (1992) 'Organic synthesis: past, present and future', Chemistry and Industry, pp. 903–906. [Google Scholar] [Crossref]

16. Sheldon, R.A. (2018) 'Metrics of green chemistry and sustainability: past, present, and future', ACS Sustainable Chemistry and Engineering, 6(1), pp. 32–48. [Google Scholar] [Crossref]

17. Trost, B.M. (1991) 'The atom economy: a search for synthetic efficiency', Science, 254(5037), pp. 1471–1477. [Google Scholar] [Crossref]

18. Wernet, G. et al. (2016) 'Life cycle assessment of fine chemical production: a case study of pharmaceutical synthesis', International Journal of Life Cycle Assessment, 15(3), pp. 294–303. [Google Scholar] [Crossref]

• What the Desert Fathers Teach Data Scientists: Ancient Ascetic Principles for Ethical Machine-Learning Practice
• Comparative Analysis of Some Machine Learning Algorithms for the Classification of Ransomware
• Comparative Performance Analysis of Some Priority Queue Variants in Dijkstra’s Algorithm
• Transfer Learning in Detecting E-Assessment Malpractice from a Proctored Video Recordings.
• Dual-Modal Detection of Parkinson’s Disease: A Clinical Framework and Deep Learning Approach Using NeuroParkNet