Artificial Intelligence–Driven Green Synthesis of pharmaceutically Important Chemical Compounds

The growing demand for sustainable and environmentally friendly chemical processes has led to 
significant interest in green synthesis approaches, particularly in the development of medicinally important 
chemical compounds. Traditional synthesis methods often involve toxic solvents, harsh reaction conditions, and 
high energy consumption, which pose risks to both human health and the environment. Green chemistry aims to 
minimize these drawbacks by employing eco-friendly reagents, renewable resources, and safer reaction 
pathways. In recent years, artificial intelligence (AI) has emerged as a powerful tool to accelerate and optimize 
green synthesis strategies. AI techniques such as machine learning, data mining, and predictive modeling enable 
researchers to analyze complex datasets, identify optimal synthesis conditions, and predict the biological activity 
and stability of metal complexes. When combined with green synthesis methods such as plant-mediated, 
microbial, or solvent-free approaches AI helps reduce trial-and-error experimentation, saving time, cost, and 
resources. Moreover, AI-assisted design supports the rational selection of metal ions, ligands, and reaction 
parameters to enhance medicinal properties like antimicrobial, anticancer, and antioxidant activity. By 
integrating AI with green chemistry principles, researchers can develop metal complexes that are not only 
biologically effective but also environmentally sustainable, marking a significant advancement in modern 
medicinal and coordination chemistry.