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.